A latent model for collaborative filtering

Recommender systems based on collaborative filtering have received a great deal of interest over the last decade. Typically, these types of systems either take a user-centered or an item-centered approach when making recommendations, but by employing only one of these two perspectives we may unintentionally leave out important information that could otherwise have improved the recommendations. In this paper, we propose a collaborative filtering model that contains an explicit representation of all items and users. Experimental results show that the proposed system obtains significantly better results than other collaborative filtering systems (evaluated on the MovieLens data set). Furthermore, the explicit representation of all users and items allows the model to e.g. make group-based recommendations balancing the preferences of the individual users.