Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text. Extended version http://i.stanford.edu/~julian/pdfs/ recsys_extended.pdf

In order to recommend products to users we must ultimately pre-dict how a user will respond to a new product. To do so we must uncover the implicit tastes of each user as well as the properties of each product. For example, in order to predict whether a user will enjoy Harry Potter, it helps to identify that the book is about wiz-ards, as well as the user’s level of interest in wizardry. User feed-back is required to discover these latent product and user dimen-sions. Such feedback often comes in the form of a numeric rating accompanied by review text. However, traditional methods often discard review text, which makes user and product latent dimen-sions difficult to interpret, since they ignore the very text that justi-fies a user’s rating. In this paper, we aim to combine latent rating dimensions (such as those of latent-factor recommender systems) with latent review topics (such as those learned by topic models like LDA). Our approach has several advantages. Firstly, we ob-tain highly interpretable textual labels for latent rating dimensions, which helps us to ‘justify ’ ratings with text. Secondly, our approach more accurately predicts product ratings by harnessing the informa-tion present in review text; this is especially true for new products and users, who may have too few ratings to model their latent fac-tors, yet may still provide substantial information from the text of even a single review. Thirdly, our discovered topics can be used to facilitate other tasks such as automated genre discovery, and to identify useful and representative reviews