updated bpr in readme

Author: blondered

README.md

@@ -104,6 +104,7 @@ See [recommender baselines extended tutorial](https://github.com/MobileTeleSyste
 | Model | Type | Description (🎏 for user/item features, 🔆 for warm inference, ❄️ for cold inference support) | Tutorials & Benchmarks |
 |----|----|---------|--------|
 | [implicit](https://github.com/benfred/implicit) ALS Wrapper | Matrix Factorization | `rectools.models.ImplicitALSWrapperModel` - Alternating Least Squares Matrix Factorizattion algorithm for implicit feedback. <br>🎏| 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Implicit-ALS)<br> 🚀 [50% boost to metrics with user & item features](examples/5_benchmark_iALS_with_features.ipynb) |
+| [implicit](https://github.com/benfred/implicit) BPR-MF Wrapper | Matrix Factorization | `rectools.models.ImplicitBPRWrapperModel` - Bayesian Personalized Ranking Matrix Factorization algorithm. | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#Bayesian-Personalized-Ranking-Matrix-Factorization-(BPR-MF)) |
 | [implicit](https://github.com/benfred/implicit) ItemKNN Wrapper | Nearest Neighbours | `rectools.models.ImplicitItemKNNWrapperModel` - Algorithm that calculates item-item similarity matrix using distances between item vectors in user-item interactions matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#ItemKNN) |
 | [LightFM](https://github.com/lyst/lightfm) Wrapper | Matrix Factorization | `rectools.models.LightFMWrapperModel` - Hybrid matrix factorization algorithm which utilises user and item features and supports a variety of losses.<br>🎏 🔆 ❄️| 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#LightFM)<br>🚀 [10-25 times faster inference with RecTools](examples/6_benchmark_lightfm_inference.ipynb)|
 | EASE | Linear Autoencoder | `rectools.models.EASEModel` - Embarassingly Shallow Autoencoders implementation that explicitly calculates dense item-item similarity matrix | 📙 [Theory & Practice](https://rectools.readthedocs.io/en/latest/examples/tutorials/baselines_extended_tutorial.html#EASE) |