Abstract
Collaborative filtering plays a crucial role in reducing excessive information in online consuming by
suggesting products to customers that fulfil their
potential interests. Observing that users’ reviews
on their purchases are often in companion with ratings, recent works exploit the review texts in modelling user or item factors and have achieved prominent performance. Although effectiveness of reviews has been verified, one major defect of existing works is that reviews are used in justifying
the learning of either user or item factors without
noticing that each review associates a pair of user
and item concurrently. To better explore the value
of review comments, this paper presents the privileged matrix factorization method that utilize reviews in the learning of both user and item factors. By mapping review texts into the privileged
feature space, a learned privileged function compensates the discrepancies between predicted ratings and groundtruth values rating-wisely. Thus by
minimizing discrepancies and prediction errors, our
method harnesses the information present in the review comments for the learning of both user and
item factors. Experiments on five real datasets testify the effectiveness of the proposed method