  Both reviews and user-item interactions (i.e., rating scores) have been
widely adopted for user rating prediction. However, these existing techniques
mainly extract the latent representations for users and items in an independent
and static manner. That is, a single static feature vector is derived to encode
her preference without considering the particular characteristics of each
candidate item. We argue that this static encoding scheme is difficult to fully
capture the users' preference. In this paper, we propose a novel context-aware
user-item representation learning model for rating prediction, named CARL.
Namely, CARL derives a joint representation for a given user-item pair based on
their individual latent features and latent feature interactions. Then, CARL
adopts Factorization Machines to further model higher-order feature
interactions on the basis of the user-item pair for rating prediction.
Specifically, two separate learning components are devised in CARL to exploit
review data and interaction data respectively: review-based feature learning
and interaction-based feature learning. In review-based learning component,
with convolution operations and attention mechanism, the relevant features for
a user-item pair are extracted by jointly considering their corresponding
reviews. However, these features are only review-driven and may not be
comprehensive. Hence, interaction-based learning component further extracts
complementary features from interaction data alone, also on the basis of
user-item pairs. The final rating score is then derived with a dynamic linear
fusion mechanism. Experiments on five real-world datasets show that CARL
achieves significantly better rating prediction accuracy than existing
state-of-the-art alternatives. Also, with attention mechanism, we show that the
relevant information in reviews can be highlighted to interpret the rating
prediction.
