  Recent studies in recommender systems have managed to achieve significantly
improved performance by leveraging reviews for rating prediction. However,
despite being extensively studied, these methods still suffer from some
limitations. First, previous studies either encode the document or extract
latent sentiment via neural networks, which are difficult to interpret the
sentiment of reviewers intuitively. Second, they neglect the personalized
interaction of reviews with user/item, i.e., each review has different
contributions when modeling the sentiment preference of user/item. To remedy
these issues, we propose a Sentiment-aware Interactive Fusion Network (SIFN)
for review-based item recommendation. Specifically, we first encode user/item
reviews via BERT and propose a light-weighted sentiment learner to extract
semantic features of each review. Then, we propose a sentiment prediction task
that guides the sentiment learner to extract sentiment-aware features via
explicit sentiment labels. Finally, we design a rating prediction task that
contains a rating learner with an interactive and fusion module to fuse the
identity (i.e., user and item ID) and each review representation so that
various interactive features can synergistically influence the final rating
score. Experimental results on five real-world datasets demonstrate that the
proposed model is superior to state-of-the-art models.
