Machine Learning for Building Recommender System in Python

In this article, I use the Kaggle Netflix prize data [2] to demonstrate how to use model-based collaborative filtering method to build a recommender system in Python. Recommender systems are widely used in product recommendations such as recommendations of music, movies, books, news, research articles, restaurants, etc. [1][5]. The collaborative filtering method [5] predicts (filters) the interests of a user on a product by collecting preferences information from many other users (collaborating). The assumption behind the collaborative filtering method is that if a person P1 has the same opinion as another person P2 on an issue, P1 is more likely to share P2's opinion on a different issue than that of a randomly chosen person [5]. Content-based filtering method [6] utilizes product features/attributes to recommend other products similar to what the user likes, based on other users' previous actions or explicit feedback such as rating on products.