SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation

TL;DR

The paper introduces SIFN, a novel review-based recommendation model that explicitly incorporates sentiment analysis and interactive review fusion to improve rating prediction accuracy.

Contribution

It proposes a sentiment-aware interactive fusion network that uses BERT and explicit sentiment labels for more interpretable and personalized review modeling in recommendations.

Findings

Outperforms state-of-the-art models on five datasets.

Effectively captures sentiment and interaction for better ratings.

Demonstrates the importance of personalized review interaction.

Abstract

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.