Hybrid recommenders: incorporating metadata awareness into latent factor models

Abstract

This paper proposes a hybrid recommender algorithm which integrates a set of different user's inputs into a unified and generic latent factor model to improve prediction accuracy. The technique can exploit users' demographics, such as age, gender and occupation, along with implicit feedback and items' metadata. Depending on the personal information from users, the recommender selects content whose subject is semantically related to their interests. The method was evaluated in the MovieLens dataset and compared against other approaches reported in the literature. The results show the effectiveness of incorporating metadata awareness into a latent factor model.