Pretty cool! These look like pretty good recommendations. Mathematically, it decomposes \(R\) into two unitary matrices and a diagonal matrix:\ ) return user_full, recommendations already_rated, predictions = recommend_movies ( preds_df, 837, movies_df, ratings_df, 10 ) smaller/simpler) approximation of the original matrix \(R\).
![any movie recommendations any movie recommendations](https://pbs.twimg.com/media/Ef1eTh9XgAAuYcg.jpg)
So what is singular value decomposition (SVD)? At a high level, SVD is an algorithm that decomposes a matrix \(R\) into the best lower rank (i.e. There are many different ways to factor matrices, but singular value decomposition is particularly useful for making recommendations. It’s extremely well studied in mathematics, and it’s highly useful.
![any movie recommendations any movie recommendations](https://img-9gag-fun.9cache.com/photo/aV73NMK_460s.jpg)
Matrix factorization is the breaking down of one matrix into a product of multiple matrices. Matrix Factorization via Singular Value Decomposition Low-Rank Matrix Factorization is that kind of method. Stealing an example from Joseph Konston (professor at Minnesota who has a Coursera course on recommender systems), what if we both like songs with great storytelling, regardless of the genre? How do we resolve this? I need a method that can derive tastes and preference vectors from the raw data. Using item features (such as genre) could help fix this issue, but not entirely. We’d be in entirely separate neighborhoods, even though it seems pretty likely we share at least some underlying preferences. Mathematically, the dot product of our action vectors would be 0. It’s a subtle difference, but it’s important.įor example, if I’ve listened to ten Red Hot Chili Peppers songs and you’ve listened to ten different Red Hot Chili Peppers songs, the raw user action matrix wouldn’t have any overlap. When we use distance based “neighborhood” approaches on raw data, we match on sparse, low-level details that we assume represent the user’s preference vectors instead of matching on the vectors themselves. The key concern is that ratings matrices may be overfit and noisy representations of user tastes and preferences. I talked about the scaling issue in the previous post, but not the conceptual issue. There’s a theoretical concern with raw data based approaches.
![any movie recommendations any movie recommendations](https://files.getfishbowl.com/content_preview_images/any-movie-recommendations-on-netflix-preferably-something-funny-tia.jpg)