Weighted aspect-based collaborative filtering

Yanping Nie, Yang Liu, Xiaohui Yu

Abstract: Existing work on collaborative filtering (CF) is often based on the overall ratings the items have received. However, in many cases, understanding how a user rates each aspect of an item may reveal more detailed information about her preferences and thus may lead to more effective CF. Prior work has studied extracting/quantizing sentiments on different aspects from the reviews, based on which the unknown overall ratings are inferred. However, in that work, all the aspects are treated equally; while in reality, different users tend to place emphases on difference aspects when reaching the overall rating. For example, users may give a high rating to a movie just for its plot despite its mediocre performances. This emphasis on aspects varies for different users and different items. In this paper, we propose a method that uses tensor factorization to automatically infer the weights of different aspects in forming the overall rating. The main idea is to learn, through constrained optimization, a compact representation of a weight tensor indexed by three dimensions for user, item, and aspect, respectively. Overall ratings can then be predicted using the obtained weights. Experiments on a movie dataset show that our method compares favorably with three baseline methods.