Paper Info
Title
SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation
Abstract
BSTRACTRecent studies in recommender systems have managed to achieve significantly improved performance. However, despite being extensively studied, these methods still suffer from two 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 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 demonstrate that the proposed model is superior to state-of-the-art models.
Year
DOI
Venue
2021
10.1145/3459637.3482181
Conference on Information and Knowledge Management
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Kai Zhang122.74
Hao Qian221.38
Liu Qi31027106.48
Zhiqiang Zhang49514.65
Jun Zhou5101.90
Jianhui Ma6239.52
Enhong Chen72106165.57