  Utilizing review information to enhance recommendation, the de facto
review-involved recommender systems, have received increasing interests over
the past few years. Thereinto, one advanced branch is to extract salient
aspects from textual reviews (i.e., the item attributes that users express) and
combine them with the matrix factorization technique. However, existing
approaches all ignore the fact that semantically different reviews often
include opposite aspect information. In particular, positive reviews usually
express aspects that users prefer, while negative ones describe aspects that
users reject. As a result, it may mislead the recommender systems into making
incorrect decisions pertaining to user preference modeling. Towards this end,
in this paper, we propose a Review Polarity-wise Recommender model, dubbed as
RPR, to discriminately treat reviews with different polarities. To be specific,
in this model, positive and negative reviews are separately gathered and
utilized to model the user-preferred and user-rejected aspects, respectively.
Besides, in order to overcome the imbalance problem of semantically different
reviews, we also develop an aspect-aware importance weighting approach to align
the aspect importance for these two kinds of reviews. Extensive experiments
conducted on eight benchmark datasets have demonstrated the superiority of our
model as compared to a series of state-of-the-art review-involved baselines.
Moreover, our method can provide certain explanations to the real-world rating
prediction scenarios.
