Learning persona-driven personalized sentimental representation for review-based recommendation
- Publisher:
- PERGAMON-ELSEVIER SCIENCE LTD
- Publication Type:
- Journal Article
- Citation:
- Expert Systems with Applications, 2022, 203
- Issue Date:
- 2022-10-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Learning persona-driven personalized sentimental representation for review-based recommendation.pdf | Accepted version | 2.32 MB |
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A large amount of information exists in many e-commerce and review websites as a valuable source for recommender systems. Recent solutions focus on exploring the correlation between sentiment and textual reviews in the review-based recommendation. However, these studies usually pay less attention to the differences of different users in sentimental expression styles or language usage habits when a user writes reviews. In this work, we argue that the individual reviewing behavior is closely related to personality, and sentimental expression is a manifestation of personality. Therefore, we propose a novel Persona-driven Sentimental Attentive Recommendation model (named PSAR) via personalized sentimental interactive representation learning for the review-based recommendation. The proposed model is devised to learn fragment-level and sequence-level personalized sentimental representation simultaneously from reviews. Besides, an attentive persona-driven interaction module is designed to capture word-level usage habits and sentence-level analogous tones. Comprehensive experimental results on four real-world datasets demonstrate that our model outperforms the state-of-the-art methods.
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