Now, between collaborative and content-based, which one should you choose? Collaborative filtering is able to learn user preferences from a user's actions regarding one content source, and use them across other content types. Content-based filtering is limited to recommending content of the same type that the user is already using. This provides value in certain use cases; for example, recommending news articles based on news browsing is useful, but it is much more useful if different sources, such as books and movies, can be recommended based on news browsing.

Collaborative filtering and content-based filtering are not mutually exclusive; they can be combined to be more effective in some cases. For example, Netflix uses collaborative filtering to analyze the searching and watching patterns of similar users, as well as content-based filtering to offer movies that share characteristics with films that the user has rated highly.

There is a wide variety of hybridization techniques: weighted, switching, and mixed, feature combination, feature augmentation, cascade, meta-level, and so on. Recommendation systems are an active area in the machine learning and data mining community, with special tracks on data science conferences. A good overview of techniques is summarized in the paper Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions, by Adomavicius and Tuzhilin (2005), where the authors discuss different approaches and underlying algorithms, and provide references to further papers. To get more technical and understand all of the tiny details when a particular approach makes sense, you should look at the book edited by Ricci, et al.: Recommender Systems Handbook (First Edition, 2010, Springer-Verlag, New York).