A Review-Level Sentiment Information Enhanced Multitask Learning Approach for Explainable Recommendation
Bibliographic Data
| ID | 22108902 |
|---|---|
| Authors | Fenfang Xie (0000-0001-7011-8512, Sun Yat-sen University), Yuansheng Wang (0000-0002-8117-2642, Sun Yat-sen University), Kun Xu (0000-0002-0851-2495, Sun Yat-sen University), Liang Chen (0000-0003-1553-2846, Sun Yat-sen University), Zibin Zheng (0000-0001-7872-7718, Sun Yat-sen University), Mingdong Tang (0009-0005-3713-113X, Guangdong University of Foreign Studies) |
| Year | 2024 |
| Volume | 11 |
| Issue | 5 |
| Pages | 5925-5934 |
| Publication date | 2024-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2024.3376728 |
| OpenAlex | W4395680467 |
| Language | EN |
| Citations received | 2 |
| References cited | 42 |
Recommendation system plays a remarkable role in solving the problem of information overload on the Internet. Existing research demonstrates that a recommended list enclosed with appropriate explanations can enhance the transparency of the system and encourage users to make decisions. Although existing works have achieved effective results, they still suffer from at least one of the following limitations: the work either does not use sentiment information or review information, does not explicitly incorporate review-level sentiment information into the model, is based on review retrieval, and generates explanations in the form of templates or phrases. To tackle the above limitations, this article proposes a REview-level Sentiment information enhanced multiTask learning approach for Explainable Recommendation (RESTER). Specifically, it first considers the user’s review information and analyzes the sentiment polarity contained in the review. Then, the user/item’s identity feature, review feature, and sentiment information are fused into a multitask learning framework by leveraging the implicit correlation between the rating prediction and explanation generation tasks. Comprehensive experiments on datasets in three different domains have shown that the proposed model is superior to all other baselines in both rating prediction and explanation generation tasks
Multi-task learning · Systems engineering · Advanced Graph Neural Networks · Computer Science · Engineering · Recommender Systems and Techniques · Topic Modeling · Artificial Intelligence
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |