A Word is Worth a Thousand Ratings: Augmenting Ratings using Reviews for Collaborative Filtering

In order to provide personalized recommendations, collaborative filtering algorithms take into account several kinds of feedback from the user. A common kind of feedback, which was largely neglected by the Academic community until recently, is textual reviews that are written by the users. Reviews may reveal a great deal about both the users and the items, and indeed in recent years, several algorithms that make use of textual reviews were proposed. However, it is not entirely clear how this signal should be combined with traditional methods that address other kinds of feedback (such as an explicit numeric rating). In this paper, we introduce a novel algorithm, named Collaborative Filtering using Compatibility Vectors (CFCV), which builds upon recent advances in natural language understanding, and uses a neural network in order to provide a meaningful representation of the reviews. This allows to enhance collaborative filtering (particularly, factor methods ) with this new kind of information, in a way that is both natural and effective. We validate our algorithm by conducting experiments on several benchmark datasets, showing that it outperforms the existing methods. Moreover, underlying our solution there is a general architecture that may be further explored.