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Recommender-systems with Rsparse

Collaborative filtering

The goal for this project is to produce recommendation using collaborative filtering (matrix factorization & cosine distance).

Project is made from three parts:

  1. cross-validation, wich contains the tunning grid needed for ALS and calculates the accuracy using NDCG

  2. making_reco, generates recommendation user based (ALS) and item based (cosine distance)

  3. api_reco_als, which will deliver ALS recommendation for a user using an API

The library used for recommendation is https://github.com/rexyai/rsparse fallowing this excellent post http://dsnotes.com/post/2017-05-28-matrix-factorization-for-recommender-systems/.

For the API the RestRserve library was used https://restrserve.org/articles/RestRserve.html.

The model runs extremly fast, it can generate recommendation in ~ 300 secs for 138286 users for ALS and in ~ 200 secs for 8546 movies using cosine distance.