collaborative-filtering-deep-dive.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"from fastai.collab import *\nfrom fastai.tabular.all import *\nset_seed(42)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:53.713771Z","iopub.execute_input":"2022-06-27T22:34:53.714291Z","iopub.status.idle":"2022-06-27T22:34:58.125346Z","shell.execute_reply.started":"2022-06-27T22:34:53.714191Z","shell.execute_reply":"2022-06-27T22:34:58.123923Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"> *This is chapter 8 of the book [Practical Deep Learning for Coders](https://www.amazon.com/Deep-Learning-Coders-fastai-PyTorch/dp/1492045527), provided courtesy of O'Reilly Media. The full book is [available as Jupyter Notebooks](https://github.com/fastai/fastbook/). A free course that covers the book is [available here](https://course.fast.ai/).*\n>\n> Click **Copy and Edit** in the top right to run the code in this notebook yourself.","metadata":{}},{"cell_type":"markdown","source":"One very common problem to solve is when you have a number of users and a number of products, and you want to recommend which products are most likely to be useful for which users. There are many variations of this: for example, recommending movies (such as on Netflix), figuring out what to highlight for a user on a home page, deciding what stories to show in a social media feed, and so forth. There is a general solution to this problem, called *collaborative filtering*, which works like this: look at what products the current user has used or liked, find other users that have used or liked similar products, and then recommend other products that those users have used or liked.\n\nFor example, on Netflix you may have watched lots of movies that are science fiction, full of action, and were made in the 1970s. Netflix may not know these particular properties of the films you have watched, but it will be able to see that other people that have watched the same movies that you watched also tended to watch other movies that are science fiction, full of action, and were made in the 1970s. In other words, to use this approach we don't necessarily need to know anything about the movies, except who like to watch them.\n\nThere is actually a more general class of problems that this approach can solve, not necessarily involving users and products. Indeed, for collaborative filtering we more commonly refer to *items*, rather than *products*. Items could be links that people click on, diagnoses that are selected for patients, and so forth.\n\nThe key foundational idea is that of *latent factors*. In the Netflix example, we started with the assumption that you like old, action-packed sci-fi movies. But you never actually told Netflix that you like these kinds of movies. And Netflix never actually needed to add columns to its movies table saying which movies are of these types. Still, there must be some underlying concept of sci-fi, action, and movie age, and these concepts must be relevant for at least some people's movie watching decisions.","metadata":{}},{"cell_type":"markdown","source":"For this chapter we are going to work on this movie recommendation problem. We'll start by getting some data suitable for a collaborative filtering model.","metadata":{}},{"cell_type":"markdown","source":"## A First Look at the Data","metadata":{}},{"cell_type":"markdown","source":"We do not have access to Netflix's entire dataset of movie watching history, but there is a great dataset that we can use, called [MovieLens](https://grouplens.org/datasets/movielens/). This dataset contains tens of millions of movie rankings (a combination of a movie ID, a user ID, and a numeric rating), although we will just use a subset of 100,000 of them for our example. If you're interested, it would be a great learning project to try and replicate this approach on the full 25-million recommendation dataset, which you can get from their website.","metadata":{}},{"cell_type":"markdown","source":"The dataset is available through the usual fastai function:","metadata":{}},{"cell_type":"code","source":"path = untar_data(URLs.ML_100k)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:53.713771Z","iopub.execute_input":"2022-06-27T22:34:53.714291Z","iopub.status.idle":"2022-06-27T22:34:58.125346Z","shell.execute_reply.started":"2022-06-27T22:34:53.714191Z","shell.execute_reply":"2022-06-27T22:34:58.123923Z"},"trusted":true},"execution_count":1,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n    <div>\n      <progress value='4931584' class='' max='4924029' style='width:300px; height:20px; vertical-align: middle;'></progress>\n      100.15% [4931584/4924029 00:00<00:00]\n    </div>\n    "},"metadata":{}}]},{"cell_type":"markdown","source":"According to the *README*, the main table is in the file *u.data*. It is tab-separated and the columns are, respectively user, movie, rating, and timestamp. Since those names are not encoded, we need to indicate them when reading the file with Pandas. Here is a way to open this table and take a look:","metadata":{}},{"cell_type":"code","source":"ratings = pd.read_csv(path/'u.data', delimiter='\\t', header=None,\n                      names=['user','movie','rating','timestamp'])\nratings.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.130450Z","iopub.execute_input":"2022-06-27T22:34:58.131806Z","iopub.status.idle":"2022-06-27T22:34:58.221177Z","shell.execute_reply.started":"2022-06-27T22:34:58.131756Z","shell.execute_reply":"2022-06-27T22:34:58.219087Z"},"trusted":true},"execution_count":2,"outputs":[{"execution_count":2,"output_type":"execute_result","data":{"text/plain":"   user  movie  rating  timestamp\n0   196    242       3  881250949\n1   186    302       3  891717742\n2    22    377       1  878887116\n3   244     51       2  880606923\n4   166    346       1  886397596","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user</th>\n      <th>movie</th>\n      <th>rating</th>\n      <th>timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>196</td>\n      <td>242</td>\n      <td>3</td>\n      <td>881250949</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>186</td>\n      <td>302</td>\n      <td>3</td>\n      <td>891717742</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>22</td>\n      <td>377</td>\n      <td>1</td>\n      <td>878887116</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>244</td>\n      <td>51</td>\n      <td>2</td>\n      <td>880606923</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>166</td>\n      <td>346</td>\n      <td>1</td>\n      <td>886397596</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"Although this has all the information we need, it is not a particularly helpful way for humans to look at this data. Here is the same data cross-tabulated into a human-friendly table:","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:9cbc69a5-40ca-4406-9bde-0db7c9887870.png)","metadata":{},"attachments":{"9cbc69a5-40ca-4406-9bde-0db7c9887870.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"We have selected just a few of the most popular movies, and users who watch the most movies, for this crosstab example. The empty cells in this table are the things that we would like our model to learn to fill in. Those are the places where a user has not reviewed the movie yet, presumably because they have not watched it. For each user, we would like to figure out which of those movies they might be most likely to enjoy.\n\nIf we knew for each user to what degree they liked each important category that a movie might fall into, such as genre, age, preferred directors and actors, and so forth, and we knew the same information about each movie, then a simple way to fill in this table would be to multiply this information together for each movie and use a combination. For instance, assuming these factors range between -1 and +1, with positive numbers indicating stronger matches and negative numbers weaker ones, and the categories are science-fiction, action, and old movies, then we could represent the movie *The Last Skywalker* as:","metadata":{}},{"cell_type":"code","source":"last_skywalker = np.array([0.98,0.9,-0.9])","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.225102Z","iopub.execute_input":"2022-06-27T22:34:58.227736Z","iopub.status.idle":"2022-06-27T22:34:58.239654Z","shell.execute_reply.started":"2022-06-27T22:34:58.227659Z","shell.execute_reply":"2022-06-27T22:34:58.237762Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"Here, for instance, we are scoring *very science-fiction* as 0.98, *very action* as 0.9, and *very not old* as -0.9. We could represent a user who likes modern sci-fi action movies as:","metadata":{}},{"cell_type":"code","source":"user1 = np.array([0.9,0.8,-0.6])","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.252991Z","iopub.execute_input":"2022-06-27T22:34:58.253711Z","iopub.status.idle":"2022-06-27T22:34:58.272044Z","shell.execute_reply.started":"2022-06-27T22:34:58.253649Z","shell.execute_reply":"2022-06-27T22:34:58.270228Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"markdown","source":"and we can now calculate the match between this combination:","metadata":{}},{"cell_type":"code","source":"(user1*last_skywalker).sum()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.275126Z","iopub.execute_input":"2022-06-27T22:34:58.276203Z","iopub.status.idle":"2022-06-27T22:34:58.286255Z","shell.execute_reply.started":"2022-06-27T22:34:58.276144Z","shell.execute_reply":"2022-06-27T22:34:58.284957Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"2.1420000000000003"},"metadata":{}}]},{"cell_type":"markdown","source":"When we multiply two vectors together and add up the results, this is known as the *dot product*. It is used a lot in machine learning, and forms the basis of matrix multiplication. We will be looking a lot more at matrix multiplication and dot products in <<chapter_foundations>>.","metadata":{}},{"cell_type":"markdown","source":"> jargon: dot product: The mathematical operation of multiplying the elements of two vectors together, and then summing up the result.","metadata":{}},{"cell_type":"markdown","source":"On the other hand, we might represent the movie *Casablanca* as:","metadata":{}},{"cell_type":"code","source":"casablanca = np.array([-0.99,-0.3,0.8])","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.288634Z","iopub.execute_input":"2022-06-27T22:34:58.289675Z","iopub.status.idle":"2022-06-27T22:34:58.300906Z","shell.execute_reply.started":"2022-06-27T22:34:58.289616Z","shell.execute_reply":"2022-06-27T22:34:58.299112Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"The match between this combination is:","metadata":{}},{"cell_type":"code","source":"(user1*casablanca).sum()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.303405Z","iopub.execute_input":"2022-06-27T22:34:58.304716Z","iopub.status.idle":"2022-06-27T22:34:58.316422Z","shell.execute_reply.started":"2022-06-27T22:34:58.304653Z","shell.execute_reply":"2022-06-27T22:34:58.314972Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"-1.611"},"metadata":{}}]},{"cell_type":"markdown","source":"Since we don't know what the latent factors actually are, and we don't know how to score them for each user and movie, we should learn them.","metadata":{}},{"cell_type":"markdown","source":"## Learning the Latent Factors","metadata":{}},{"cell_type":"markdown","source":"There is surprisingly little difference between specifying the structure of a model, as we did in the last section, and learning one, since we can just use our general gradient descent approach.\n\nStep 1 of this approach is to randomly initialize some parameters. These parameters will be a set of latent factors for each user and movie. We will have to decide how many to use. We will discuss how to select this shortly, but for illustrative purposes let's use 5 for now. Because each user will have a set of these factors and each movie will have a set of these factors, we can show these randomly initialized values right next to the users and movies in our crosstab, and we can then fill in the dot products for each of these combinations in the middle. For example, this is what it looks like in Microsoft Excel, with the top-left cell formula displayed as an 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9PI5LbY/se9uyIEpMi36pv0mx116mzdfjBifpLc7+ljC6mv2gxFtMUfnx11hMXaPzujvLudcGrk8w0pUHuNIp/8Mmh8+o7rLoQPLMEQi21krI5acuz/ruMXGo3V3Qxw96VMim9ff24glVy1fgIEOBvFsoeC0pFxqlqMVIjxBs+mcfhjWYx3CD4qZMdOy7VhPqtBpMKLzwGC8Gi3tA0Ei9pAnY7z7tXOrNWt322nm6It3EmQmIeeR45Atd6T9FU88+1CuEicmt8ezBaepoc0Qqb9an8oMv24chuOgXHMX9ojV3qHlJeH7IJwPud3oa1ZyNce0LSJQL8lfJ0gmpNwehXpJsrwkab1kIWQP+SoZ1qosaAHnJbtb1wi0vf2MkvzFxxvJxjl+p5P2A+DH9Dbv4aFv1nQ63dZTOQmmG7KFyNiyjDf0797Lp/oP3/3kIimWyFz8pHdYb9zv+1uMvr5LRET7dODia/2z8pxeZvpGFIzdI/r5vbuERizPsBKVx9jTsT+DRH/unMH//D7sS9tiU2vZ2dlEIkH0h2v2k0clHH5PRInsizXHAREiotb738wPG3tWYQAAIABJREFUf/5HS0LJKctAx3uMc4igveu8xjN+XUiLhCdBS9M5tJc5BuKOUqrQNBhPSDkwF5R0+BKj/SlJV5kwLFGyMOfATZfkbE+L2UN/M4agcu9B4OfRPGTjjlS5cd3mg9dv6JmRs4dcpECiZBywe8KvIMJxHJTJbdgjVXvLkJfkr73tcOV2yxO/6+6ONEL1kp0g6wTOxkPAhOb2K9WqDPr6In+9JGoPeSoZ9qpMJuLzkrdbVzlbTJ6iJH/x8UyycY5J+nBMb70/7s2YjvGc9dcZj1/sG0TG1u+dB+S/sPvrpP7k+4mbliVLZM47CX5i1zl8s3MJ+qeUC27ftLtCDjJieYaVqDwmLq8yn8FYMjm3uvrs2bO9PWMRjlbTurz47H37xTMS2bUXQ9fVaL03X/zoGUeJXTOnrvoNAx0CGI/0iBph4EjQfHs6vJc5BhMepcHsbztI/u6NQBnzvyq5WuizlNsSvocsGcP4lf3kJsa9jP0jZwESFyX2Qx52Rxe/3mmmhP3Wi7S5PeC6MWAcYZe/9pYhL8mwDxy4cru50a0Zd+2b6NRLtka7TuAgQ26Xv16yFXBekuFMDSZsD3kqGfaqTCbi73btb10j0/a25SVK8hcfASQb5wjJL78SEdHdqK2N7D/JI3Pwg/7Dpx/bf+DT9mIGv7KsReHZ+DX9B7cvTIwWyfMMK1F5TGBeNYbZnJIaorlx7969R+vGyuSJ7Iv+NcgTs9m1F3vP5pKeFg+PXfuD8TNe6PBG0xqVovnohfeHc3gTlPplDuFR8sS887jq5H81OtA9tMkYlnkHypn5YqXRdcPVuZcJu8wJjRL7IQ95olGWPqDA8pJlJovht+By1Y3OmA6qa0OxYZej9pYhL8lfe/fzkNs7tYxyayZOWqNSnJ9PGub7KiqKTL3ULyp1QpBkz+1y1Ev9gs9Lsp8psXvIU8mwV2Vy8CcvOd26RqPt3c9jlOQvPmL8X9g7IIEWfddee4D1KemRF53I2L5XYTqjc/JlTYhfWubiHKaPaYo8Lxfvm3rnIf6//MmH1KOTZ1iJymNe0zmlO535r1a4xiCs00wRJbJr+f4XNpKrz/pGPtykN2D6K+BjvlaqU9KldS/TrHpMUM6XOYRHyY3rNxQilfTbisHfdfFOo7DnxlXLmWTZ+J+iKOO3Hi5kA9gr+V+NFruHnBkjVaiVaC1fV4nUejlfLytK+uH6QipOnb0LuSNK/HmU/pA5BJjbXY0IhFI3esA5zMEa9kjU3jLkJflrbysBud14fFc9XJsvq2r3X1W1rubrh+ncutNCJxEguk4IrWkhXOi5PRL1klVYeSn0MzVU+HsYtarM17bKoFvXSDVEhUTJW+aMVIWP9zlMn10jatFmiaZnaKzzb3qZDq78U8jSRkZ/dWDCcdGIT2748r3GxEGDV2VwNV9WwDrjEDRBaT+7pKXNM6xE5TEB6bTooEbTT/R03uRdfa9NMu+t64tfVtYf3Xu8G7XTcmUp6VuzQscQ2RI0F3GT7+EWC+FRsmU+V2f/urPcVFWtl/OZ5Hyx4e8Xyf9qtOg95M4Y8VShWisZiwqqaj2fySSTmbJKSjpXa4baO+3PeWQ6ZHPmBsfkwp4gJsDczlcZB1M3cuO8wrCHPQK1twx5Sf7aeyCe3G48N01qb8+gQa2XM+aFMwL10kCC64TAmhbCSZDbI1AvDRRQXpLgTA0heg95Khn2qkwqIvPSsIaF1G3vgXiiJDZzSl7hY5zDnDd/6wmNPaDV/a4n8U/O6M4Dmq6Fs2shG6HI/CK0H3c801lNoUzTNctEQ+2e6AfyvsxhLJ6xmPXlBZdRyjOsROWxrnROzVGisQd0p0wnRIs5On/OPy1YcnWv7UW2s3jGZeURhjqiQa2X85nMfFHYHRBTgsZ7v1K9zNFPeJTsGStCtl93bmjm17XfKNZfC5aaWs8nfWyeNoqyvxrtwx5yZwytUVzL14lIUdLprvVK1frhdpjdHr6dR3kPmUOAud16387ypGFAdSMfzoPiCrvstbcMeUn+2nsIr7ldSedKtZq5iHStZNZR9byc3TrsfKoTfG5aCCdHbpe9XhoikLwkx5kaRL49jGBVJjAvDb91jWxDlD1KfmVOSSt8zFvVZeoufX3fnIno4pT++oROiE7KtHSNXk6GunOhQmR6vHxKP3cicLNs/xnp5m6yTHkUwClDnvHJVpl+PqKvv7KbM41FbO7Z2vt7+mLkl5Xvm3P9y3S4SebaH4j0V0Qu37eIMHWVSPFstZklIiJN095tG6/WZuh6k+tBDP4ELe/9liR7hEp4lNxJFUrpun7XqdbzGef7T/uVAwOiKOlbDxdmrsfjnZ3QtMbx9qtyXW/6119VFlL+3o11v+jcp55P1incu0KRe8iVMYw3yTvfUShoWmN77VVdVamdneuHYd82iz2PkThkDr7nduN5vOEDAiHVjRwYDsoeW9gjUnvLkJfkr707vOd287npdKl3k3g8VajWbnRm/KgfNQopqYqQS8LqBBmaFsKFnNsjUi91hJmX5K+XQt3DyFVlfrVVht66RqohKixKHJkzihU+3ucwLT6lt/mursPxSXq7Q1NERLT1g8NmV8AIRGbIoggcJuntDr25S1MT5u+mJmjjKZ13IiOXVmfKI+Kf8si9COWZzWXLOxPGvxJzOqLyWFc6k/Th2Px3vkMbd4k6b8Zsen4FIzk1a/x8dNL0mlwfrN0hULzdMs3pj5p4f6udMUGp1x83CI/SYKmC9bGoHoqi6H9yWjkwCKlCtVrIpsyGKRHF46ms9TVt9fBY3ueVIok5YxjPVFs7G+PxVKFabZpJqdLOMcCB+ZCHr8Bq3tlL0QfkD2P9SFKY5lYOuG5kw3tQXkSg9vZbGGEPhm+53Vy1lupHDaJI10ueooSmhT8iWi8hL/nJj0qmvyqThdDae9ita2Tb3kG36KJZSDHOYfr5vd1vY/SXdkf2rzRscryRJXtkzug8lO+N0e08vX1udkO/fU4rkzTembvps2uh7Ja9pc5sWhs7/FMeuSd7nmElKo95S2c8Rit5Os/p/1391k0YW83djY3Hjx8/fnzPn4U4YtcSxs+/ve/6AutaIH/AOIcIZstUUHvCZYJyrj/uRHiUBnxVqlCtlXJpxXJfqijpXKnWrK7f0n8hWeeGLp5a6LSQ1V8lXE0q2tgyhrEMsF3xiqcK1ZKkN6T8ruAhC2BZhDJdqnI85Bhg3eia54PiFeHaW4DQwh4c9tw+vD+xvVb0KBHfqkTTwpMI10vIS9KIfFUmJC8NvXWNekNUhhadzIUU81YRfUxT5GJBhTM6J3+WNJCW9JH55AbRGRHRLy37OXyGLv7sh4tOF/+nHwf4rQMtzdAWEREtPqUVX3ubpc8z/Vae04rzX0XlMYF5dTxDG0e0ekZ0RvXW0BMao/dHR53hhh//3ZqbMzdonhwZP3O/b9E1cVXXF+B1jpERjZc5whJPZQupbKH311pFbz/L9uCdjYt3Gvmx0l6q0Gz2BcbQKCbbTxP3v1cfGD/30HXGMG61HLNKajZN9TpRSFMMiI8SzyFfv6EQqaTfS9lkV2PhzXCKnN+5vatjWtL1V5l5PyhvYZe09pYhL8lfe/vJ/TVR9nopVH41LYSTLLdLWi+FSs9Lkp0pGz7soZdKJjJlULyht67St72F87n4yJbZ8D4HUYw+IyKik8FjUMH2lUtB+sjc7NRK//nd7s8t+u6MiIhuBNqlXm93HU9QWo6O3aVlc5DD91UxpM8zrETlMbF59ZMbA1PrZn3f4rJS2mi2X7loNXcfr5vDHB7et7DOfnVZKe22v6DV3C1VjNc5Ep//UY7iABw665ZF42UOWZjt51sz0gdNzmcDR9OgjOH8MJTcT955wHDI5gOKF+/sHlyTdnoYEXo7pkfiAKU9qEjV3sykDbsEjFrH/YOpV7peGuYKHrKPRrteGuYq5yWOSoajKhsxLLeuV7Dt7QvZCinGOYiIPm3PpbNPB/1/C6mvXBKSR2a8MzGU7XoPFz/pLxYsfhHYHhGd0uoZEdHUrBQZZmmZts6IghnkICLp8wwrUXlMbF7VX/5w985QbO6+OQ5Bl0frj+7du3fv3qN1cxSCKJH9kmcR8jbrQAddVtpf0JU+hjmEEX7LPDRB873f6LzMIUHHgvkckbw3pFf5MdPQDM4Y9vewNMrniuGQh9y4j26MzI5pJV1qeuqYlqBu7OyJuIMSLQq1Ny+Jwy4ex6T1M7c6853bT1NidDWPTr0kvk6Q/pCjKQL1EvKST9grGZ6qTCoe8xLjrWtU295StOjkjVHExzlOaXqGxmZoqeYpmXSnk+6bvnQOXofRV+7dFYnMJG10etX712T+e1n/4c/W/n1BkbFnrPU9Qf/I+JA+o02mQY4rkmdYicpjrOmc0tgyHdgtp3FR08fSXL8z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2 of this approach is to calculate our predictions. As we've discussed, we can do this by simply taking the dot product of each movie with each user. If, for instance, the first latent user factor represents how much the user likes action movies and the first latent movie factor represents if the movie has a lot of action or not, the product of those will be particularly high if either the user likes action movies and the movie has a lot of action in it or the user doesn't like action movies and the movie doesn't have any action in it. On the other hand, if we have a mismatch (a user loves action movies but the movie isn't an action film, or the user doesn't like action movies and it is one), the product will be very low.\n\nStep 3 is to calculate our loss. We can use any loss function that we wish; let's pick mean squared error for now, since that is one reasonable way to represent the accuracy of a prediction.\n\nThat's all we need. With this in place, we can optimize our parameters (that is, the latent factors) using stochastic gradient descent, such as to minimize the loss. At each step, the stochastic gradient descent optimizer will calculate the match between each movie and each user using the dot product, and will compare it to the actual rating that each user gave to each movie. It will then calculate the derivative of this value and will step the weights by multiplying this by the learning rate. After doing this lots of times, the loss will get better and better, and the recommendations will also get better and better.","metadata":{}},{"cell_type":"markdown","source":"To use the usual `Learner.fit` function we will need to get our data into a `DataLoaders`, so let's focus on that now.","metadata":{}},{"cell_type":"markdown","source":"## Creating the DataLoaders","metadata":{}},{"cell_type":"markdown","source":"When showing the data, we would rather see movie titles than their IDs. The table `u.item` contains the correspondence of IDs to titles:","metadata":{}},{"cell_type":"code","source":"movies = pd.read_csv(path/'u.item',  delimiter='|', encoding='latin-1',\n                     usecols=(0,1), names=('movie','title'), header=None)\nmovies.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.318129Z","iopub.execute_input":"2022-06-27T22:34:58.319778Z","iopub.status.idle":"2022-06-27T22:34:58.346197Z","shell.execute_reply.started":"2022-06-27T22:34:58.319709Z","shell.execute_reply":"2022-06-27T22:34:58.344940Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"   movie              title\n0      1   Toy Story (1995)\n1      2   GoldenEye (1995)\n2      3  Four Rooms (1995)\n3      4  Get Shorty (1995)\n4      5     Copycat (1995)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>movie</th>\n      <th>title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>Toy Story (1995)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>GoldenEye (1995)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>Four Rooms (1995)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>Get Shorty (1995)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>Copycat (1995)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"We can merge this with our `ratings` table to get the user ratings by title:","metadata":{}},{"cell_type":"code","source":"ratings = ratings.merge(movies)\nratings.head()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.348114Z","iopub.execute_input":"2022-06-27T22:34:58.348935Z","iopub.status.idle":"2022-06-27T22:34:58.401981Z","shell.execute_reply.started":"2022-06-27T22:34:58.348883Z","shell.execute_reply":"2022-06-27T22:34:58.400605Z"},"trusted":true},"execution_count":9,"outputs":[{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"   user  movie  rating  timestamp         title\n0   196    242       3  881250949  Kolya (1996)\n1    63    242       3  875747190  Kolya (1996)\n2   226    242       5  883888671  Kolya (1996)\n3   154    242       3  879138235  Kolya (1996)\n4   306    242       5  876503793  Kolya (1996)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user</th>\n      <th>movie</th>\n      <th>rating</th>\n      <th>timestamp</th>\n      <th>title</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>196</td>\n      <td>242</td>\n      <td>3</td>\n      <td>881250949</td>\n      <td>Kolya (1996)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>63</td>\n      <td>242</td>\n      <td>3</td>\n      <td>875747190</td>\n      <td>Kolya (1996)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>226</td>\n      <td>242</td>\n      <td>5</td>\n      <td>883888671</td>\n      <td>Kolya (1996)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>154</td>\n      <td>242</td>\n      <td>3</td>\n      <td>879138235</td>\n      <td>Kolya (1996)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>306</td>\n      <td>242</td>\n      <td>5</td>\n      <td>876503793</td>\n      <td>Kolya (1996)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"We can then build a `DataLoaders` object from this table. By default, it takes the first column for the user, the second column for the item (here our movies), and the third column for the ratings. We need to change the value of `item_name` in our case to use the titles instead of the IDs:","metadata":{}},{"cell_type":"code","source":"dls = CollabDataLoaders.from_df(ratings, item_name='title', bs=64)\ndls.show_batch()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.406029Z","iopub.execute_input":"2022-06-27T22:34:58.407132Z","iopub.status.idle":"2022-06-27T22:34:58.659819Z","shell.execute_reply.started":"2022-06-27T22:34:58.407085Z","shell.execute_reply":"2022-06-27T22:34:58.658426Z"},"trusted":true},"execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>user</th>\n      <th>title</th>\n      <th>rating</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>518</td>\n      <td>Richard III (1995)</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>546</td>\n      <td>Star Wars (1977)</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>264</td>\n      <td>Adventures of Priscilla, Queen of the Desert, The (1994)</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>201</td>\n      <td>Kolya (1996)</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>664</td>\n      <td>Dances with Wolves (1990)</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>391</td>\n      <td>Jerry Maguire (1996)</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>401</td>\n      <td>Beauty and the Beast (1991)</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>771</td>\n      <td>Strictly Ballroom (1992)</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>330</td>\n      <td>101 Dalmatians (1996)</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>594</td>\n      <td>One Flew Over the Cuckoo's Nest (1975)</td>\n      <td>4</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"To represent collaborative filtering in PyTorch we can't just use the crosstab representation directly, especially if we want it to fit into our deep learning framework. We can represent our movie and user latent factor tables as simple matrices:","metadata":{}},{"cell_type":"code","source":"n_users  = len(dls.classes['user'])\nn_movies = len(dls.classes['title'])\nn_factors = 5\n\nuser_factors = torch.randn(n_users, n_factors)\nmovie_factors = torch.randn(n_movies, n_factors)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.674575Z","iopub.execute_input":"2022-06-27T22:34:58.675858Z","iopub.status.idle":"2022-06-27T22:34:58.687494Z","shell.execute_reply.started":"2022-06-27T22:34:58.675814Z","shell.execute_reply":"2022-06-27T22:34:58.686168Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"markdown","source":"To calculate the result for a particular movie and user combination, we have to look up the index of the movie in our movie latent factor matrix and the index of the user in our user latent factor matrix; then we can do our dot product between the two latent factor vectors. But *look up in an index* is not an operation our deep learning models know how to do. They know how to do matrix products, and activation functions.\n\nFortunately, it turns out that we can represent *look up in an index* as a matrix product. The trick is to replace our indices with one-hot-encoded vectors. Here is an example of what happens if we multiply a vector by a one-hot-encoded vector representing the index 3:","metadata":{}},{"cell_type":"code","source":"one_hot_3 = one_hot(3, n_users).float()","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.689208Z","iopub.execute_input":"2022-06-27T22:34:58.689934Z","iopub.status.idle":"2022-06-27T22:34:58.704790Z","shell.execute_reply.started":"2022-06-27T22:34:58.689892Z","shell.execute_reply":"2022-06-27T22:34:58.703432Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"user_factors.t() @ one_hot_3","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.706572Z","iopub.execute_input":"2022-06-27T22:34:58.707841Z","iopub.status.idle":"2022-06-27T22:34:58.730934Z","shell.execute_reply.started":"2022-06-27T22:34:58.707783Z","shell.execute_reply":"2022-06-27T22:34:58.729791Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"tensor([-1.2493, -0.3099,  1.4229,  0.0840,  0.4132])"},"metadata":{}}]},{"cell_type":"markdown","source":"It gives us the same vector as the one at index 3 in the matrix:","metadata":{}},{"cell_type":"code","source":"user_factors[3]","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.732360Z","iopub.execute_input":"2022-06-27T22:34:58.733288Z","iopub.status.idle":"2022-06-27T22:34:58.745511Z","shell.execute_reply.started":"2022-06-27T22:34:58.733241Z","shell.execute_reply":"2022-06-27T22:34:58.744287Z"},"trusted":true},"execution_count":15,"outputs":[{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"tensor([-1.2493, -0.3099,  1.4229,  0.0840,  0.4132])"},"metadata":{}}]},{"cell_type":"markdown","source":"If we do that for a few indices at once, we will have a matrix of one-hot-encoded vectors, and that operation will be a matrix multiplication! This would be a perfectly acceptable way to build models using this kind of architecture, except that it would use a lot more memory and time than necessary. We know that there is no real underlying reason to store the one-hot-encoded vector, or to search through it to find the occurrence of the number one—we should just be able to index into an array directly with an integer. Therefore, most deep learning libraries, including PyTorch, include a special layer that does just this; it indexes into a vector using an integer, but has its derivative calculated in such a way that it is identical to what it would have been if it had done a matrix multiplication with a one-hot-encoded vector. This is called an *embedding*.","metadata":{}},{"cell_type":"markdown","source":"> jargon: Embedding: Multiplying by a one-hot-encoded matrix, using the computational shortcut that it can be implemented by simply indexing directly. This is quite a fancy word for a very simple concept. The thing that you multiply the one-hot-encoded matrix by (or, using the computational shortcut, index into directly) is called the _embedding matrix_.","metadata":{}},{"cell_type":"markdown","source":"In computer vision, we have a very easy way to get all the information of a pixel through its RGB values: each pixel in a colored image is represented by three numbers. Those three numbers give us the redness, the greenness and the blueness, which is enough to get our model to work afterward.\n\nFor the problem at hand, we don't have the same easy way to characterize a user or a movie. There are probably relations with genres: if a given user likes romance, they are likely to give higher scores to romance movies. Other factors might be whether the movie is more action-oriented versus heavy on dialogue, or the presence of a specific actor that a user might particularly like. \n\nHow do we determine numbers to characterize those? The answer is, we don't. We will let our model *learn* them. By analyzing the existing relations between users and movies, our model can figure out itself the features that seem important or not.\n\nThis is what embeddings are. We will attribute to each of our users and each of our movies a random vector of a certain length (here, `n_factors=5`), and we will make those learnable parameters. That means that at each step, when we compute the loss by comparing our predictions to our targets, we will compute the gradients of the loss with respect to those embedding vectors and update them with the rules of SGD (or another optimizer).\n\nAt the beginning, those numbers don't mean anything since we have chosen them randomly, but by the end of training, they will. By learning on existing data about the relations between users and movies, without having any other information, we will see that they still get some important features, and can isolate blockbusters from independent cinema, action movies from romance, and so on.\n\nWe are now in a position that we can create our whole model from scratch.","metadata":{}},{"cell_type":"markdown","source":"## Collaborative Filtering from Scratch","metadata":{}},{"cell_type":"markdown","source":"Before we can write a model in PyTorch, we first need to learn the basics of object-oriented programming and Python. If you haven't done any object-oriented programming before, we will give you a quick introduction here, but we would recommend looking up a tutorial and getting some practice before moving on.\n\nThe key idea in object-oriented programming is the *class*. We have been using classes throughout this book, such as `DataLoader`, `string`, and `Learner`. Python also makes it easy for us to create new classes. Here is an example of a simple class:","metadata":{}},{"cell_type":"code","source":"class Example:\n    def __init__(self, a): self.a = a\n    def say(self,x): return f'Hello {self.a}, {x}.'","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.747619Z","iopub.execute_input":"2022-06-27T22:34:58.748745Z","iopub.status.idle":"2022-06-27T22:34:58.756531Z","shell.execute_reply.started":"2022-06-27T22:34:58.748680Z","shell.execute_reply":"2022-06-27T22:34:58.754847Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"The most important piece of this is the special method called `__init__` (pronounced *dunder init*). In Python, any method surrounded in double underscores like this is considered special. It indicates that there is some extra behavior associated with this method name. In the case of `__init__`, this is the method Python will call when your new object is created. So, this is where you can set up any state that needs to be initialized upon object creation. Any parameters included when the user constructs an instance of your class will be passed to the `__init__` method as parameters. Note that the first parameter to any method defined inside a class is `self`, so you can use this to set and get any attributes that you will need:","metadata":{}},{"cell_type":"code","source":"ex = Example('Sylvain')\nex.say('nice to meet you')","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.758928Z","iopub.execute_input":"2022-06-27T22:34:58.760249Z","iopub.status.idle":"2022-06-27T22:34:58.772044Z","shell.execute_reply.started":"2022-06-27T22:34:58.760197Z","shell.execute_reply":"2022-06-27T22:34:58.770382Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"'Hello Sylvain, nice to meet you.'"},"metadata":{}}]},{"cell_type":"markdown","source":"Also note that creating a new PyTorch module requires inheriting from `Module`. *Inheritance* is an important object-oriented concept that we will not discuss in detail here—in short, it means that we can add additional behavior to an existing class. PyTorch already provides a `Module` class, which provides some basic foundations that we want to build on. So, we add the name of this *superclass* after the name of the class that we are defining, as shown in the following example.\n\nThe final thing that you need to know to create a new PyTorch module is that when your module is called, PyTorch will call a method in your class called `forward`, and will pass along to that any parameters that are included in the call. Here is the class defining our dot product model:","metadata":{}},{"cell_type":"code","source":"class DotProduct(Module):\n    def __init__(self, n_users, n_movies, n_factors):\n        self.user_factors = Embedding(n_users, n_factors)\n        self.movie_factors = Embedding(n_movies, n_factors)\n        \n    def forward(self, x):\n        users = self.user_factors(x[:,0])\n        movies = self.movie_factors(x[:,1])\n        return (users * movies).sum(dim=1)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.774287Z","iopub.execute_input":"2022-06-27T22:34:58.775190Z","iopub.status.idle":"2022-06-27T22:34:58.786753Z","shell.execute_reply.started":"2022-06-27T22:34:58.775128Z","shell.execute_reply":"2022-06-27T22:34:58.785104Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"markdown","source":"If you haven't seen object-oriented programming before, then don't worry, you won't need to use it much in this book. We are just mentioning this approach here, because most online tutorials and documentation will use the object-oriented syntax.\n\nNote that the input of the model is a tensor of shape `batch_size x 2`, where the first column (`x[:, 0]`) contains the user IDs and the second column (`x[:, 1]`) contains the movie IDs. As explained before, we use the *embedding* layers to represent our matrices of user and movie latent factors:","metadata":{}},{"cell_type":"code","source":"x,y = dls.one_batch()\nx.shape","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.788806Z","iopub.execute_input":"2022-06-27T22:34:58.789513Z","iopub.status.idle":"2022-06-27T22:34:58.925995Z","shell.execute_reply.started":"2022-06-27T22:34:58.789442Z","shell.execute_reply":"2022-06-27T22:34:58.924739Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"torch.Size([64, 2])"},"metadata":{}}]},{"cell_type":"markdown","source":"Now that we have defined our architecture, and created our parameter matrices, we need to create a `Learner` to optimize our model. In the past we have used special functions, such as `cnn_learner`, which set up everything for us for a particular application. Since we are doing things from scratch here, we will use the plain `Learner` class:","metadata":{}},{"cell_type":"code","source":"model = DotProduct(n_users, n_movies, 50)\nlearn = Learner(dls, model, loss_func=MSELossFlat())","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.927744Z","iopub.execute_input":"2022-06-27T22:34:58.928149Z","iopub.status.idle":"2022-06-27T22:34:58.941859Z","shell.execute_reply.started":"2022-06-27T22:34:58.928115Z","shell.execute_reply":"2022-06-27T22:34:58.940397Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":"We are now ready to fit our model:","metadata":{}},{"cell_type":"code","source":"learn.fit_one_cycle(5, 5e-3)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:34:58.944564Z","iopub.execute_input":"2022-06-27T22:34:58.945513Z","iopub.status.idle":"2022-06-27T22:35:48.927727Z","shell.execute_reply.started":"2022-06-27T22:34:58.945433Z","shell.execute_reply":"2022-06-27T22:35:48.926507Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>1.391418</td>\n      <td>1.281064</td>\n      <td>00:10</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>1.071581</td>\n      <td>1.059740</td>\n      <td>00:10</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.925082</td>\n      <td>0.955736</td>\n      <td>00:10</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.789137</td>\n      <td>0.877227</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.742079</td>\n      <td>0.862848</td>\n      <td>00:09</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"The first thing we can do to make this model a little bit better is to force those predictions to be between 0 and 5. For this, we just need to use `sigmoid_range`, like in <<chapter_multicat>>. One thing we discovered empirically is that it's better to have the range go a little bit over 5, so we use `(0, 5.5)`:","metadata":{}},{"cell_type":"code","source":"class DotProduct(Module):\n    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n        self.user_factors = Embedding(n_users, n_factors)\n        self.movie_factors = Embedding(n_movies, n_factors)\n        self.y_range = y_range\n        \n    def forward(self, x):\n        users = self.user_factors(x[:,0])\n        movies = self.movie_factors(x[:,1])\n        return sigmoid_range((users * movies).sum(dim=1), *self.y_range)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:35:48.929572Z","iopub.execute_input":"2022-06-27T22:35:48.931919Z","iopub.status.idle":"2022-06-27T22:35:48.940594Z","shell.execute_reply.started":"2022-06-27T22:35:48.931876Z","shell.execute_reply":"2022-06-27T22:35:48.939325Z"},"trusted":true},"execution_count":22,"outputs":[]},{"cell_type":"code","source":"model = DotProduct(n_users, n_movies, 50)\nlearn = Learner(dls, model, loss_func=MSELossFlat())\nlearn.fit_one_cycle(5, 5e-3)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:35:48.941998Z","iopub.execute_input":"2022-06-27T22:35:48.942352Z","iopub.status.idle":"2022-06-27T22:36:34.061813Z","shell.execute_reply.started":"2022-06-27T22:35:48.942322Z","shell.execute_reply":"2022-06-27T22:36:34.060680Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.978663</td>\n      <td>0.978617</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.858417</td>\n      <td>0.889232</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.657446</td>\n      <td>0.861478</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.473589</td>\n      <td>0.862058</td>\n      <td>00:08</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.372989</td>\n      <td>0.865978</td>\n      <td>00:08</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"This is a reasonable start, but we can do better. One obvious missing piece is that some users are just more positive or negative in their recommendations than others, and some movies are just plain better or worse than others. But in our dot product representation we do not have any way to encode either of these things. If all you can say about a movie is, for instance, that it is very sci-fi, very action-oriented, and very not old, then you don't really have any way to say whether most people like it. \n\nThat's because at this point we only have weights; we do not have biases. If we have a single number for each user that we can add to our scores, and ditto for each movie, that will handle this missing piece very nicely. So first of all, let's adjust our model architecture:","metadata":{}},{"cell_type":"code","source":"class DotProductBias(Module):\n    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n        self.user_factors = Embedding(n_users, n_factors)\n        self.user_bias = Embedding(n_users, 1)\n        self.movie_factors = Embedding(n_movies, n_factors)\n        self.movie_bias = Embedding(n_movies, 1)\n        self.y_range = y_range\n        \n    def forward(self, x):\n        users = self.user_factors(x[:,0])\n        movies = self.movie_factors(x[:,1])\n        res = (users * movies).sum(dim=1, keepdim=True)\n        res += self.user_bias(x[:,0]) + self.movie_bias(x[:,1])\n        return sigmoid_range(res, *self.y_range)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:36:34.063194Z","iopub.execute_input":"2022-06-27T22:36:34.063529Z","iopub.status.idle":"2022-06-27T22:36:34.073745Z","shell.execute_reply.started":"2022-06-27T22:36:34.063500Z","shell.execute_reply":"2022-06-27T22:36:34.072598Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"markdown","source":"Let's try training this and see how it goes:","metadata":{}},{"cell_type":"code","source":"model = DotProductBias(n_users, n_movies, 50)\nlearn = Learner(dls, model, loss_func=MSELossFlat())\nlearn.fit_one_cycle(5, 5e-3)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:36:34.075433Z","iopub.execute_input":"2022-06-27T22:36:34.075791Z","iopub.status.idle":"2022-06-27T22:37:18.123472Z","shell.execute_reply.started":"2022-06-27T22:36:34.075761Z","shell.execute_reply":"2022-06-27T22:37:18.122528Z"},"trusted":true},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.943364</td>\n      <td>0.932987</td>\n      <td>00:08</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.824034</td>\n      <td>0.864243</td>\n      <td>00:08</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.586939</td>\n      <td>0.860469</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.409691</td>\n      <td>0.886897</td>\n      <td>00:08</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.303943</td>\n      <td>0.894082</td>\n      <td>00:08</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"Instead of being better, it ends up being worse (at least at the end of training). Why is that? If we look at both trainings carefully, we can see the validation loss stopped improving in the middle and started to get worse. As we've seen, this is a clear indication of overfitting. In this case, there is no way to use data augmentation, so we will have to use another regularization technique. One approach that can be helpful is *weight decay*.","metadata":{}},{"cell_type":"markdown","source":"### Weight Decay","metadata":{}},{"cell_type":"markdown","source":"Weight decay, or *L2 regularization*, consists in adding to your loss function the sum of all the weights squared. Why do that? Because when we compute the gradients, it will add a contribution to them that will encourage the weights to be as small as possible.\n\nWhy would it prevent overfitting? The idea is that the larger the coefficients are, the sharper canyons we will have in the loss function. If we take the basic example of a parabola, `y = a * (x**2)`, the larger `a` is, the more *narrow* the parabola is:","metadata":{}},{"cell_type":"code","source":"x = np.linspace(-2,2,100)\na_s = [1,2,5,10,50] \nys = [a * x**2 for a in a_s]\n_,ax = plt.subplots(figsize=(8,6))\nfor a,y in zip(a_s,ys): ax.plot(x,y, label=f'a={a}')\nax.set_ylim([0,5])\nax.legend();","metadata":{"hide_input":true,"execution":{"iopub.status.busy":"2022-06-27T22:37:18.125086Z","iopub.execute_input":"2022-06-27T22:37:18.125918Z","iopub.status.idle":"2022-06-27T22:37:18.392132Z","shell.execute_reply.started":"2022-06-27T22:37:18.125873Z","shell.execute_reply":"2022-06-27T22:37:18.390905Z"},"trusted":true},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 576x432 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"So, letting our model learn high parameters might cause it to fit all the data points in the training set with an overcomplex function that has very sharp changes, which will lead to overfitting.\n\nLimiting our weights from growing too much is going to hinder the training of the model, but it will yield a state where it generalizes better. Going back to the theory briefly, weight decay (or just `wd`) is a parameter that controls that sum of squares we add to our loss (assuming `parameters` is a tensor of all parameters):\n\n``` python\nloss_with_wd = loss + wd * (parameters**2).sum()\n```\n\nIn practice, though, it would be very inefficient (and maybe numerically unstable) to compute that big sum and add it to the loss. If you remember a little bit of high school math, you might recall that the derivative of `p**2` with respect to `p` is `2*p`, so adding that big sum to our loss is exactly the same as doing:\n\n``` python\nparameters.grad += wd * 2 * parameters\n```\n\nIn practice, since `wd` is a parameter that we choose, we can just make it twice as big, so we don't even need the `*2` in this equation. To use weight decay in fastai, just pass `wd` in your call to `fit` or `fit_one_cycle`:","metadata":{}},{"cell_type":"code","source":"model = DotProductBias(n_users, n_movies, 50)\nlearn = Learner(dls, model, loss_func=MSELossFlat())\nlearn.fit_one_cycle(5, 5e-3, wd=0.1)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:37:18.393669Z","iopub.execute_input":"2022-06-27T22:37:18.393975Z","iopub.status.idle":"2022-06-27T22:38:01.696224Z","shell.execute_reply.started":"2022-06-27T22:37:18.393949Z","shell.execute_reply":"2022-06-27T22:38:01.695416Z"},"trusted":true},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.964149</td>\n      <td>0.947329</td>\n      <td>00:08</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.853209</td>\n      <td>0.862615</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.734107</td>\n      <td>0.828079</td>\n      <td>00:08</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.595621</td>\n      <td>0.812455</td>\n      <td>00:08</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.490830</td>\n      <td>0.812497</td>\n      <td>00:08</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"Much better!","metadata":{}},{"cell_type":"markdown","source":"### Creating Our Own Embedding Module","metadata":{}},{"cell_type":"markdown","source":"So far, we've used `Embedding` without thinking about how it really works. Let's re-create `DotProductBias` *without* using this class. We'll need a randomly initialized weight matrix for each of the embeddings. We have to be careful, however. Recall from <<chapter_mnist_basics>> that optimizers require that they can get all the parameters of a module from the module's `parameters` method. However, this does not happen fully automatically. If we just add a tensor as an attribute to a `Module`, it will not be included in `parameters`:","metadata":{}},{"cell_type":"code","source":"class T(Module):\n    def __init__(self): self.a = torch.ones(3)\n\nL(T().parameters())","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:01.702734Z","iopub.execute_input":"2022-06-27T22:38:01.703068Z","iopub.status.idle":"2022-06-27T22:38:01.710362Z","shell.execute_reply.started":"2022-06-27T22:38:01.703038Z","shell.execute_reply":"2022-06-27T22:38:01.709553Z"},"trusted":true},"execution_count":28,"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"(#0) []"},"metadata":{}}]},{"cell_type":"markdown","source":"To tell `Module` that we want to treat a tensor as a parameter, we have to wrap it in the `nn.Parameter` class. This class doesn't actually add any functionality (other than automatically calling `requires_grad_` for us). It's only used as a \"marker\" to show what to include in `parameters`:","metadata":{}},{"cell_type":"code","source":"class T(Module):\n    def __init__(self): self.a = nn.Parameter(torch.ones(3))\n\nL(T().parameters())","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:01.711604Z","iopub.execute_input":"2022-06-27T22:38:01.711907Z","iopub.status.idle":"2022-06-27T22:38:01.722702Z","shell.execute_reply.started":"2022-06-27T22:38:01.711880Z","shell.execute_reply":"2022-06-27T22:38:01.721745Z"},"trusted":true},"execution_count":29,"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"(#1) [Parameter containing:\ntensor([1., 1., 1.], requires_grad=True)]"},"metadata":{}}]},{"cell_type":"markdown","source":"All PyTorch modules use `nn.Parameter` for any trainable parameters, which is why we haven't needed to explicitly use this wrapper up until now:","metadata":{}},{"cell_type":"code","source":"class T(Module):\n    def __init__(self): self.a = nn.Linear(1, 3, bias=False)\n\nt = T()\nL(t.parameters())","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:01.724155Z","iopub.execute_input":"2022-06-27T22:38:01.724459Z","iopub.status.idle":"2022-06-27T22:38:01.732462Z","shell.execute_reply.started":"2022-06-27T22:38:01.724433Z","shell.execute_reply":"2022-06-27T22:38:01.731739Z"},"trusted":true},"execution_count":30,"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"(#1) [Parameter containing:\ntensor([[-0.2714],\n        [ 0.3146],\n        [ 0.0898]], requires_grad=True)]"},"metadata":{}}]},{"cell_type":"code","source":"type(t.a.weight)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:01.733772Z","iopub.execute_input":"2022-06-27T22:38:01.734122Z","iopub.status.idle":"2022-06-27T22:38:01.742943Z","shell.execute_reply.started":"2022-06-27T22:38:01.734094Z","shell.execute_reply":"2022-06-27T22:38:01.741920Z"},"trusted":true},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":"torch.nn.parameter.Parameter"},"metadata":{}}]},{"cell_type":"markdown","source":"We can create a tensor as a parameter, with random initialization, like so:","metadata":{}},{"cell_type":"code","source":"def create_params(size):\n    return nn.Parameter(torch.zeros(*size).normal_(0, 0.01))","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:01.744116Z","iopub.execute_input":"2022-06-27T22:38:01.744455Z","iopub.status.idle":"2022-06-27T22:38:01.757499Z","shell.execute_reply.started":"2022-06-27T22:38:01.744428Z","shell.execute_reply":"2022-06-27T22:38:01.756435Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"markdown","source":"Let's use this to create `DotProductBias` again, but without `Embedding`:","metadata":{}},{"cell_type":"code","source":"class DotProductBias(Module):\n    def __init__(self, n_users, n_movies, n_factors, y_range=(0,5.5)):\n        self.user_factors = create_params([n_users, n_factors])\n        self.user_bias = create_params([n_users])\n        self.movie_factors = create_params([n_movies, n_factors])\n        self.movie_bias = create_params([n_movies])\n        self.y_range = y_range\n        \n    def forward(self, x):\n        users = self.user_factors[x[:,0]]\n        movies = self.movie_factors[x[:,1]]\n        res = (users*movies).sum(dim=1)\n        res += self.user_bias[x[:,0]] + self.movie_bias[x[:,1]]\n        return sigmoid_range(res, *self.y_range)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:01.758880Z","iopub.execute_input":"2022-06-27T22:38:01.759200Z","iopub.status.idle":"2022-06-27T22:38:01.769728Z","shell.execute_reply.started":"2022-06-27T22:38:01.759172Z","shell.execute_reply":"2022-06-27T22:38:01.768694Z"},"trusted":true},"execution_count":33,"outputs":[]},{"cell_type":"markdown","source":"Then let's train it again to check we get around the same results we saw in the previous section:","metadata":{}},{"cell_type":"code","source":"model = DotProductBias(n_users, n_movies, 50)\nlearn = Learner(dls, model, loss_func=MSELossFlat())\nlearn.fit_one_cycle(5, 5e-3, wd=0.1)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:01.771413Z","iopub.execute_input":"2022-06-27T22:38:01.772178Z","iopub.status.idle":"2022-06-27T22:38:50.810730Z","shell.execute_reply.started":"2022-06-27T22:38:01.772148Z","shell.execute_reply":"2022-06-27T22:38:50.809559Z"},"trusted":true},"execution_count":34,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.946225</td>\n      <td>0.933384</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.866075</td>\n      <td>0.868197</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.704416</td>\n      <td>0.825246</td>\n      <td>00:10</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.604527</td>\n      <td>0.817444</td>\n      <td>00:10</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.499887</td>\n      <td>0.817852</td>\n      <td>00:09</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"Now, let's take a look at what our model has learned.","metadata":{}},{"cell_type":"markdown","source":"## Interpreting Embeddings and Biases","metadata":{}},{"cell_type":"markdown","source":"Our model is already useful, in that it can provide us with movie recommendations for our users—but it is also interesting to see what parameters it has discovered. The easiest to interpret are the biases. Here are the movies with the lowest values in the bias vector:","metadata":{}},{"cell_type":"code","source":"movie_bias = learn.model.movie_bias.squeeze()\nidxs = movie_bias.argsort()[:5]\n[dls.classes['title'][i] for i in idxs]","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:50.812657Z","iopub.execute_input":"2022-06-27T22:38:50.813069Z","iopub.status.idle":"2022-06-27T22:38:50.828351Z","shell.execute_reply.started":"2022-06-27T22:38:50.813029Z","shell.execute_reply":"2022-06-27T22:38:50.827118Z"},"trusted":true},"execution_count":35,"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"['Children of the Corn: The Gathering (1996)',\n 'Home Alone 3 (1997)',\n 'Crow: City of Angels, The (1996)',\n 'Mortal Kombat: Annihilation (1997)',\n 'Cable Guy, The (1996)']"},"metadata":{}}]},{"cell_type":"markdown","source":"Think about what this means. What it's saying is that for each of these movies, even when a user is very well matched to its latent factors (which, as we will see in a moment, tend to represent things like level of action, age of movie, and so forth), they still generally don't like it. We could have simply sorted the movies directly by their average rating, but looking at the learned bias tells us something much more interesting. It tells us not just whether a movie is of a kind that people tend not to enjoy watching, but that people tend not to like watching it even if it is of a kind that they would otherwise enjoy! By the same token, here are the movies with the highest bias:","metadata":{}},{"cell_type":"code","source":"idxs = movie_bias.argsort(descending=True)[:5]\n[dls.classes['title'][i] for i in idxs]","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:50.830078Z","iopub.execute_input":"2022-06-27T22:38:50.830836Z","iopub.status.idle":"2022-06-27T22:38:50.839456Z","shell.execute_reply.started":"2022-06-27T22:38:50.830794Z","shell.execute_reply":"2022-06-27T22:38:50.838293Z"},"trusted":true},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"['Titanic (1997)',\n \"Schindler's List (1993)\",\n 'Shawshank Redemption, The (1994)',\n 'Star Wars (1977)',\n 'L.A. Confidential (1997)']"},"metadata":{}}]},{"cell_type":"markdown","source":"So, for instance, even if you don't normally enjoy detective movies, you might enjoy *LA Confidential*!\n\nIt is not quite so easy to directly interpret the embedding matrices. There are just too many factors for a human to look at. But there is a technique that can pull out the most important underlying *directions* in such a matrix, called *principal component analysis* (PCA). We will not be going into this in detail in this book, because it is not particularly important for you to understand to be a deep learning practitioner, but if you are interested then we suggest you check out the fast.ai course [Computational Linear Algebra for Coders](https://github.com/fastai/numerical-linear-algebra). Here's what our movies look like based on two of the strongest PCA components.","metadata":{}},{"cell_type":"code","source":"g = ratings.groupby('title')['rating'].count()\ntop_movies = g.sort_values(ascending=False).index.values[:1000]\ntop_idxs = tensor([learn.dls.classes['title'].o2i[m] for m in top_movies])\nmovie_w = learn.model.movie_factors[top_idxs].cpu().detach()\nmovie_pca = movie_w.pca(3)\nfac0,fac1,fac2 = movie_pca.t()\nidxs = list(range(50))\nX = fac0[idxs]\nY = fac2[idxs]\nplt.figure(figsize=(12,12))\nplt.scatter(X, Y)\nfor i, x, y in zip(top_movies[idxs], X, Y):\n    plt.text(x,y,i, color=np.random.rand(3)*0.7, fontsize=11)\nplt.show()","metadata":{"hide_input":true,"execution":{"iopub.status.busy":"2022-06-27T22:38:50.841578Z","iopub.execute_input":"2022-06-27T22:38:50.842400Z","iopub.status.idle":"2022-06-27T22:38:51.801456Z","shell.execute_reply.started":"2022-06-27T22:38:50.842348Z","shell.execute_reply":"2022-06-27T22:38:51.800397Z"},"trusted":true},"execution_count":37,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 864x864 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"We can see here that the model seems to have discovered a concept of *classic* versus *pop culture* movies, or perhaps it is *critically acclaimed* that is represented here.","metadata":{}},{"cell_type":"markdown","source":"> j: No matter how many models I train, I never stop getting moved and surprised by how these randomly initialized bunches of numbers, trained with such simple mechanics, manage to discover things about my data all by themselves. It almost seems like cheating, that I can create code that does useful things without ever actually telling it how to do those things!","metadata":{}},{"cell_type":"markdown","source":"We defined our model from scratch to teach you what is inside, but you can directly use the fastai library to build it. We'll look at how to do that next.","metadata":{}},{"cell_type":"markdown","source":"### Using fastai.collab","metadata":{}},{"cell_type":"markdown","source":"We can create and train a collaborative filtering model using the exact structure shown earlier by using fastai's `collab_learner`:","metadata":{}},{"cell_type":"code","source":"learn = collab_learner(dls, n_factors=50, y_range=(0, 5.5))","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:51.802843Z","iopub.execute_input":"2022-06-27T22:38:51.803740Z","iopub.status.idle":"2022-06-27T22:38:51.814462Z","shell.execute_reply.started":"2022-06-27T22:38:51.803699Z","shell.execute_reply":"2022-06-27T22:38:51.813161Z"},"trusted":true},"execution_count":38,"outputs":[]},{"cell_type":"code","source":"learn.fit_one_cycle(5, 5e-3, wd=0.1)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:38:51.815959Z","iopub.execute_input":"2022-06-27T22:38:51.816514Z","iopub.status.idle":"2022-06-27T22:39:38.404979Z","shell.execute_reply.started":"2022-06-27T22:38:51.816467Z","shell.execute_reply":"2022-06-27T22:39:38.403707Z"},"trusted":true},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.949029</td>\n      <td>0.931003</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.856821</td>\n      <td>0.868256</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.750694</td>\n      <td>0.823566</td>\n      <td>00:09</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.594716</td>\n      <td>0.811999</td>\n      <td>00:08</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.485535</td>\n      <td>0.812199</td>\n      <td>00:09</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"The names of the layers can be seen by printing the model:","metadata":{}},{"cell_type":"code","source":"learn.model","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:39:38.406625Z","iopub.execute_input":"2022-06-27T22:39:38.407081Z","iopub.status.idle":"2022-06-27T22:39:38.415177Z","shell.execute_reply.started":"2022-06-27T22:39:38.407038Z","shell.execute_reply":"2022-06-27T22:39:38.414098Z"},"trusted":true},"execution_count":40,"outputs":[{"execution_count":40,"output_type":"execute_result","data":{"text/plain":"EmbeddingDotBias(\n  (u_weight): Embedding(944, 50)\n  (i_weight): Embedding(1665, 50)\n  (u_bias): Embedding(944, 1)\n  (i_bias): Embedding(1665, 1)\n)"},"metadata":{}}]},{"cell_type":"markdown","source":"We can use these to replicate any of the analyses we did in the previous section—for instance:","metadata":{}},{"cell_type":"code","source":"movie_bias = learn.model.i_bias.weight.squeeze()\nidxs = movie_bias.argsort(descending=True)[:5]\n[dls.classes['title'][i] for i in idxs]","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:39:38.416693Z","iopub.execute_input":"2022-06-27T22:39:38.417433Z","iopub.status.idle":"2022-06-27T22:39:38.430146Z","shell.execute_reply.started":"2022-06-27T22:39:38.417392Z","shell.execute_reply":"2022-06-27T22:39:38.429375Z"},"trusted":true},"execution_count":41,"outputs":[{"execution_count":41,"output_type":"execute_result","data":{"text/plain":"['Titanic (1997)',\n \"Schindler's List (1993)\",\n 'Shawshank Redemption, The (1994)',\n 'Casablanca (1942)',\n 'Silence of the Lambs, The (1991)']"},"metadata":{}}]},{"cell_type":"markdown","source":"Another interesting thing we can do with these learned embeddings is to look at _distance_.","metadata":{}},{"cell_type":"markdown","source":"### Embedding Distance","metadata":{}},{"cell_type":"markdown","source":"On a two-dimensional map we can calculate the distance between two coordinates using the formula of Pythagoras: $\\sqrt{x^{2}+y^{2}}$ (assuming that *x* and *y* are the distances between the coordinates on each axis). For a 50-dimensional embedding we can do exactly the same thing, except that we add up the squares of all 50 of the coordinate distances.\n\nIf there were two movies that were nearly identical, then their embedding vectors would also have to be nearly identical, because the users that would like them would be nearly exactly the same. There is a more general idea here: movie similarity can be defined by the similarity of users that like those movies. And that directly means that the distance between two movies' embedding vectors can define that similarity. We can use this to find the most similar movie to *Silence of the Lambs*:","metadata":{}},{"cell_type":"code","source":"movie_factors = learn.model.i_weight.weight\nidx = dls.classes['title'].o2i['Silence of the Lambs, The (1991)']\ndistances = nn.CosineSimilarity(dim=1)(movie_factors, movie_factors[idx][None])\nidx = distances.argsort(descending=True)[1]\ndls.classes['title'][idx]","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:39:38.432171Z","iopub.execute_input":"2022-06-27T22:39:38.432908Z","iopub.status.idle":"2022-06-27T22:39:38.449784Z","shell.execute_reply.started":"2022-06-27T22:39:38.432864Z","shell.execute_reply":"2022-06-27T22:39:38.448500Z"},"trusted":true},"execution_count":42,"outputs":[{"execution_count":42,"output_type":"execute_result","data":{"text/plain":"'8 Seconds (1994)'"},"metadata":{}}]},{"cell_type":"markdown","source":"Now that we have succesfully trained a model, let's see how to deal with the situation where we have no data for a user. How can we make recommendations to new users?","metadata":{}},{"cell_type":"markdown","source":"## Bootstrapping a Collaborative Filtering Model","metadata":{}},{"cell_type":"markdown","source":"The biggest challenge with using collaborative filtering models in practice is the *bootstrapping problem*. The most extreme version of this problem is when you have no users, and therefore no history to learn from. What products do you recommend to your very first user?\n\nBut even if you are a well-established company with a long history of user transactions, you still have the question: what do you do when a new user signs up? And indeed, what do you do when you add a new product to your portfolio? There is no magic solution to this problem, and really the solutions that we suggest are just variations of *use your common sense*. You could assign new users the mean of all of the embedding vectors of your other users, but this has the problem that that particular combination of latent factors may be not at all common (for instance, the average for the science-fiction factor may be high, and the average for the action factor may be low, but it is not that common to find people who like science-fiction without action). Better would probably be to pick some particular user to represent *average taste*.\n\nBetter still is to use a tabular model based on user meta data to construct your initial embedding vector. When a user signs up, think about what questions you could ask them that could help you to understand their tastes. Then you can create a model where the dependent variable is a user's embedding vector, and the independent variables are the results of the questions that you ask them, along with their signup metadata. We will see in the next section how to create these kinds of tabular models. (You may have noticed that when you sign up for services such as Pandora and Netflix, they tend to ask you a few questions about what genres of movie or music you like; this is how they come up with your initial collaborative filtering recommendations.)","metadata":{}},{"cell_type":"markdown","source":"One thing to be careful of is that a small number of extremely enthusiastic users may end up effectively setting the recommendations for your whole user base. This is a very common problem, for instance, in movie recommendation systems. People that watch anime tend to watch a whole lot of it, and don't watch very much else, and spend a lot of time putting their ratings on websites. As a result, anime tends to be heavily overrepresented in a lot of *best ever movies* lists. In this particular case, it can be fairly obvious that you have a problem of representation bias, but if the bias is occurring in the latent factors then it may not be obvious at all.\n\nSuch a problem can change the entire makeup of your user base, and the behavior of your system. This is particularly true because of positive feedback loops. If a small number of your users tend to set the direction of your recommendation system, then they are naturally going to end up attracting more people like them to your system. And that will, of course, amplify the original representation bias. This type of bias has a natural tendency to be amplified exponentially. You may have seen examples of company executives expressing surprise at how their online platforms rapidly deteriorated in such a way that they expressed values at odds with the values of the founders. In the presence of these kinds of feedback loops, it is easy to see how such a divergence can happen both quickly and in a way that is hidden until it is too late.\n\nIn a self-reinforcing system like this, we should probably expect these kinds of feedback loops to be the norm, not the exception. Therefore, you should assume that you will see them, plan for that, and identify up front how you will deal with these issues. Try to think about all of the ways in which feedback loops may be represented in your system, and how you might be able to identify them in your data. In the end, this is coming back to our original advice about how to avoid disaster when rolling out any kind of machine learning system. It's all about ensuring that there are humans in the loop; that there is careful monitoring, and a gradual and thoughtful rollout.","metadata":{}},{"cell_type":"markdown","source":"Our dot product model works quite well, and it is the basis of many successful real-world recommendation systems. This approach to collaborative filtering is known as *probabilistic matrix factorization* (PMF). Another approach, which generally works similarly well given the same data, is deep learning.","metadata":{}},{"cell_type":"markdown","source":"## Deep Learning for Collaborative Filtering","metadata":{}},{"cell_type":"markdown","source":"To turn our architecture into a deep learning model, the first step is to take the results of the embedding lookup and concatenate those activations together. This gives us a matrix which we can then pass through linear layers and nonlinearities in the usual way.\n\nSince we'll be concatenating the embeddings, rather than taking their dot product, the two embedding matrices can have different sizes (i.e., different numbers of latent factors). fastai has a function `get_emb_sz` that returns recommended sizes for embedding matrices for your data, based on a heuristic that fast.ai has found tends to work well in practice:","metadata":{}},{"cell_type":"code","source":"embs = get_emb_sz(dls)\nembs","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:39:38.451747Z","iopub.execute_input":"2022-06-27T22:39:38.452381Z","iopub.status.idle":"2022-06-27T22:39:38.459660Z","shell.execute_reply.started":"2022-06-27T22:39:38.452340Z","shell.execute_reply":"2022-06-27T22:39:38.458826Z"},"trusted":true},"execution_count":43,"outputs":[{"execution_count":43,"output_type":"execute_result","data":{"text/plain":"[(944, 74), (1665, 102)]"},"metadata":{}}]},{"cell_type":"markdown","source":"Let's implement this class:","metadata":{}},{"cell_type":"code","source":"class CollabNN(Module):\n    def __init__(self, user_sz, item_sz, y_range=(0,5.5), n_act=100):\n        self.user_factors = Embedding(*user_sz)\n        self.item_factors = Embedding(*item_sz)\n        self.layers = nn.Sequential(\n            nn.Linear(user_sz[1]+item_sz[1], n_act),\n            nn.ReLU(),\n            nn.Linear(n_act, 1))\n        self.y_range = y_range\n        \n    def forward(self, x):\n        embs = self.user_factors(x[:,0]),self.item_factors(x[:,1])\n        x = self.layers(torch.cat(embs, dim=1))\n        return sigmoid_range(x, *self.y_range)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:39:38.461062Z","iopub.execute_input":"2022-06-27T22:39:38.461876Z","iopub.status.idle":"2022-06-27T22:39:38.471840Z","shell.execute_reply.started":"2022-06-27T22:39:38.461834Z","shell.execute_reply":"2022-06-27T22:39:38.470932Z"},"trusted":true},"execution_count":44,"outputs":[]},{"cell_type":"markdown","source":"And use it to create a model:","metadata":{}},{"cell_type":"code","source":"model = CollabNN(*embs)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:39:38.473393Z","iopub.execute_input":"2022-06-27T22:39:38.474109Z","iopub.status.idle":"2022-06-27T22:39:38.489697Z","shell.execute_reply.started":"2022-06-27T22:39:38.474067Z","shell.execute_reply":"2022-06-27T22:39:38.488734Z"},"trusted":true},"execution_count":45,"outputs":[]},{"cell_type":"markdown","source":"`CollabNN` creates our `Embedding` layers in the same way as previous classes in this chapter, except that we now use the `embs` sizes. `self.layers` is identical to the mini-neural net we created in <<chapter_mnist_basics>> for MNIST. Then, in `forward`, we apply the embeddings, concatenate the results, and pass this through the mini-neural net. Finally, we apply `sigmoid_range` as we have in previous models.\n\nLet's see if it trains:","metadata":{}},{"cell_type":"code","source":"learn = Learner(dls, model, loss_func=MSELossFlat())\nlearn.fit_one_cycle(5, 5e-3, wd=0.01)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:39:38.491270Z","iopub.execute_input":"2022-06-27T22:39:38.491842Z","iopub.status.idle":"2022-06-27T22:40:39.973547Z","shell.execute_reply.started":"2022-06-27T22:39:38.491803Z","shell.execute_reply":"2022-06-27T22:40:39.972656Z"},"trusted":true},"execution_count":46,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.966280</td>\n      <td>0.945594</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.919992</td>\n      <td>0.901902</td>\n      <td>00:14</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.851043</td>\n      <td>0.872732</td>\n      <td>00:11</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.801476</td>\n      <td>0.857287</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.766401</td>\n      <td>0.858708</td>\n      <td>00:11</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"fastai provides this model in `fastai.collab` if you pass `use_nn=True` in your call to `collab_learner` (including calling `get_emb_sz` for you), and it lets you easily create more layers. For instance, here we're creating two hidden layers, of size 100 and 50, respectively:","metadata":{}},{"cell_type":"code","source":"learn = collab_learner(dls, use_nn=True, y_range=(0, 5.5), layers=[100,50])\nlearn.fit_one_cycle(5, 5e-3, wd=0.1)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:40:39.974805Z","iopub.execute_input":"2022-06-27T22:40:39.975559Z","iopub.status.idle":"2022-06-27T22:41:49.551372Z","shell.execute_reply.started":"2022-06-27T22:40:39.975524Z","shell.execute_reply":"2022-06-27T22:41:49.550527Z"},"trusted":true},"execution_count":47,"outputs":[{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"\n<style>\n    /* Turns off some styling */\n    progress {\n        /* gets rid of default border in Firefox and Opera. */\n        border: none;\n        /* Needs to be in here for Safari polyfill so background images work as expected. */\n        background-size: auto;\n    }\n    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n        background: #F44336;\n    }\n</style>\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<IPython.core.display.HTML object>","text/html":"<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: left;\">\n      <th>epoch</th>\n      <th>train_loss</th>\n      <th>valid_loss</th>\n      <th>time</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <td>0</td>\n      <td>0.993280</td>\n      <td>0.972087</td>\n      <td>00:13</td>\n    </tr>\n    <tr>\n      <td>1</td>\n      <td>0.932168</td>\n      <td>0.910090</td>\n      <td>00:13</td>\n    </tr>\n    <tr>\n      <td>2</td>\n      <td>0.860546</td>\n      <td>0.885197</td>\n      <td>00:12</td>\n    </tr>\n    <tr>\n      <td>3</td>\n      <td>0.808683</td>\n      <td>0.851108</td>\n      <td>00:15</td>\n    </tr>\n    <tr>\n      <td>4</td>\n      <td>0.727542</td>\n      <td>0.849919</td>\n      <td>00:14</td>\n    </tr>\n  </tbody>\n</table>"},"metadata":{}}]},{"cell_type":"markdown","source":"`learn.model` is an object of type `EmbeddingNN`. Let's take a look at fastai's code for this class:","metadata":{}},{"cell_type":"code","source":"@delegates(TabularModel)\nclass EmbeddingNN(TabularModel):\n    def __init__(self, emb_szs, layers, **kwargs):\n        super().__init__(emb_szs, layers=layers, n_cont=0, out_sz=1, **kwargs)","metadata":{"execution":{"iopub.status.busy":"2022-06-27T22:41:49.552807Z","iopub.execute_input":"2022-06-27T22:41:49.553509Z","iopub.status.idle":"2022-06-27T22:41:49.560713Z","shell.execute_reply.started":"2022-06-27T22:41:49.553444Z","shell.execute_reply":"2022-06-27T22:41:49.559570Z"},"trusted":true},"execution_count":48,"outputs":[]},{"cell_type":"markdown","source":"Wow, that's not a lot of code! This class *inherits* from `TabularModel`, which is where it gets all its functionality from. In `__init__` it calls the same method in `TabularModel`, passing `n_cont=0` and `out_sz=1`; other than that, it only passes along whatever arguments it received.","metadata":{}},{"cell_type":"markdown","source":"### Sidebar: kwargs and Delegates","metadata":{}},{"cell_type":"markdown","source":"`EmbeddingNN` includes `**kwargs` as a parameter to `__init__`. In Python `**kwargs` in a parameter list means \"put any additional keyword arguments into a dict called `kwargs`. And `**kwargs` in an argument list means \"insert all key/value pairs in the `kwargs` dict as named arguments here\". This approach is used in many popular libraries, such as `matplotlib`, in which the main `plot` function simply has the signature `plot(*args, **kwargs)`. The [`plot` documentation](https://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot) says \"The `kwargs` are `Line2D` properties\" and then lists those properties.\n\nWe're using `**kwargs` in `EmbeddingNN` to avoid having to write all the arguments to `TabularModel` a second time, and keep them in sync. However, this makes our API quite difficult to work with, because now Jupyter Notebook doesn't know what parameters are available. Consequently things like tab completion of parameter names and pop-up lists of signatures won't work.\n\nfastai resolves this by providing a special `@delegates` decorator, which automatically changes the signature of the class or function (`EmbeddingNN` in this case) to insert all of its keyword arguments into the signature.","metadata":{}},{"cell_type":"markdown","source":"### End sidebar","metadata":{}},{"cell_type":"markdown","source":"Although the results of `EmbeddingNN` are a bit worse than the dot product approach (which shows the power of carefully constructing an architecture for a domain), it does allow us to do something very important: we can now directly incorporate other user and movie information, date and time information, or any other information that may be relevant to the recommendation. That's exactly what `TabularModel` does. In fact, we've now seen that `EmbeddingNN` is just a `TabularModel`, with `n_cont=0` and `out_sz=1`. So, we'd better spend some time learning about `TabularModel`, and how to use it to get great results! We'll do that in the next chapter.","metadata":{}},{"cell_type":"markdown","source":"## Conclusion","metadata":{}},{"cell_type":"markdown","source":"For our first non-computer vision application, we looked at recommendation systems and saw how gradient descent can learn intrinsic factors or biases about items from a history of ratings. Those can then give us information about the data. \n\nWe also built our first model in PyTorch. We will do a lot more of this in the next section of the book, but first, let's finish our dive into the other general applications of deep learning, continuing with tabular data.","metadata":{}},{"cell_type":"markdown","source":"## Questionnaire","metadata":{}},{"cell_type":"markdown","source":"1. What problem does collaborative filtering solve?\n1. How does it solve it?\n1. Why might a collaborative filtering predictive model fail to be a very useful recommendation system?\n1. What does a crosstab representation of collaborative filtering data look like?\n1. Write the code to create a crosstab representation of the MovieLens data (you might need to do some web searching!).\n1. What is a latent factor? Why is it \"latent\"?\n1. What is a dot product? Calculate a dot product manually using pure Python with lists.\n1. What does `pandas.DataFrame.merge` do?\n1. What is an embedding matrix?\n1. What is the relationship between an embedding and a matrix of one-hot-encoded vectors?\n1. Why do we need `Embedding` if we could use one-hot-encoded vectors for the same thing?\n1. What does an embedding contain before we start training (assuming we're not using a pretained model)?\n1. Create a class (without peeking, if possible!) and use it.\n1. What does `x[:,0]` return?\n1. Rewrite the `DotProduct` class (without peeking, if possible!) and train a model with it.\n1. What is a good loss function to use for MovieLens? Why? \n1. What would happen if we used cross-entropy loss with MovieLens? How would we need to change the model?\n1. What is the use of bias in a dot product model?\n1. What is another name for weight decay?\n1. Write the equation for weight decay (without peeking!).\n1. Write the equation for the gradient of weight decay. Why does it help reduce weights?\n1. Why does reducing weights lead to better generalization?\n1. What does `argsort` do in PyTorch?\n1. Does sorting the movie biases give the same result as averaging overall movie ratings by movie? Why/why not?\n1. How do you print the names and details of the layers in a model?\n1. What is the \"bootstrapping problem\" in collaborative filtering?\n1. How could you deal with the bootstrapping problem for new users? For new movies?\n1. How can feedback loops impact collaborative filtering systems?\n1. When using a neural network in collaborative filtering, why can we have different numbers of factors for movies and users?\n1. Why is there an `nn.Sequential` in the `CollabNN` model?\n1. What kind of model should we use if we want to add metadata about users and items, or information such as date and time, to a collaborative filtering model?","metadata":{}},{"cell_type":"markdown","source":"### Further Research\n\n1. Take a look at all the differences between the `Embedding` version of `DotProductBias` and the `create_params` version, and try to understand why each of those changes is required. If you're not sure, try reverting each change to see what happens. (NB: even the type of brackets used in `forward` has changed!)\n1. Find three other areas where collaborative filtering is being used, and find out what the pros and cons of this approach are in those areas.\n1. Complete this notebook using the full MovieLens dataset, and compare your results to online benchmarks. See if you can improve your accuracy. Look on the book's website and the fast.ai forum for ideas. Note that there are more columns in the full dataset—see if you can use those too (the next chapter might give you ideas).\n1. Create a model for MovieLens that works with cross-entropy loss, and compare it to the model in this chapter.","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}