Learning persona-driven personalized sentimental representation for review-based recommendation

Peipei WANG, Lin LI, Ru WANG, Xinhao ZHENG, Jiaxi HE, Guandong XU

Research output: Contribution to journalArticlespeer-review

4 Citations (Scopus)


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. Copyright © 2022 Elsevier Ltd. All rights reserved.

Original languageEnglish
Article number117317
JournalExpert Systems with Applications
Publication statusPublished - Oct 2022


Wang, P., Li, L., Wang, R., Zheng, X., He, J., & Xu, G. (2022). Learning persona-driven personalized sentimental representation for review-based recommendation. Expert Systems with Applications, 203, Article 117317. https://doi.org/10.1016/j.eswa.2022.117317


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