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

arXiv: https://arxiv.org/abs/2108.08022 · HTML: https://mlsanity.com/paper/2108.08022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 18 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Joint Deep Modeling of Users and Items Using Reviews for Recommendation](https://mlsanity.com/paper/1701.04783/) (2017)
- [Multi-Pointer Co-Attention Networks for Recommendation](https://mlsanity.com/paper/1801.09251/) (2018)
- [Explainable Fashion Recommendation: A Semantic Attribute Region Guided Approach](https://mlsanity.com/paper/1905.12862/) (2019)
- [A Capsule Network for Recommendation and Explaining What You Like and Dislike](https://mlsanity.com/paper/1907.00687/) (2019)
- [Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction](https://mlsanity.com/paper/2012.06968/) (2020)
- [TransNets: Learning to Transform for Recommendation](https://mlsanity.com/paper/1704.02298/) (2017)
- [A Context-Aware User-Item Representation Learning for Item Recommendation](https://mlsanity.com/paper/1712.02342/) (2017)

## Built on by (most cited first, 1 of 1)

- [Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis](https://mlsanity.com/paper/2203.16369/) (2022)

## Abstract (arXiv)

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.