A Probabilistic Model for Collaborative Filtering with Implicit and Explicit Feedback Data

Collaborative €ltering (CF) is one of themost ecient ways for recommender systems. Typically, CF-based algorithms analyze users’ preferences and items’ aŠributes using one of two types of feedback: explicit feedback (e.g., ratings given to item by users, like/dislike) or implicit feedback (e.g., clicks, views, purchases). Explicit feedback is reliable but is extremely sparse; whereas implicit feedback is abundant but is not reliable. To leverage the sparsity of explicit feedback, in this paper, we propose a model that eciently combines explicit and implicit feedback in a uni€ed model for rating prediction. Œis model is a combination of matrix factorization and item embedding, a similar concept with word-embedding in natural language processing. Œe experiments on three realdatasets (Movilens 1M, Movielens 20M, and Bookcrossing) demonstrate that ourmethod can eciently predict ratings for items even if the ratings data is not available for them. Œe experimental results also show that our method outperforms competing methods on rating prediction task in general as well as for users and items which have few ratings.