Feature-Based Matrix Factorization

Recommendation system has been used more and more frequently in many applications recent years. With the increasing information available, not only in quantities but also in types, how to leverage these rich information to build a better recommendation system becomes a natural problem. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any line of code.