An Attentive Aspect-Based Recommendation Model With Deep Neural Network

Sigeon Yang, Qinglong Li, Haebin Lim, Jaekyeong Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

With the growth of the internet and e-commerce, online reviews have become a prevalent and rich source of information for personalized recommendations. Review text typically contains user preferences regarding various aspects of items or services. However, most previous recommendation models that used online reviews focused on general opinions, without considering the various aspects of the review text. Although previous approaches have effectively reflected the overall preferences embedded in reviews, capturing the preferences for various aspects is limited. This paper proposes an attentive aspect-based recommendation model with a deep neural network (AARN), which can capture user preferences regarding various aspects embedded in review text. The proposed model uses an advanced aspect-based sentiment analysis (ABSA) method. Unlike previous approaches that extract overall preferences, ABSA offers the advantage of extracting preferences for various aspects embedded in review texts. An attention mechanism is then applied to measure the unique attention weights for each aspect. Extensive experiments were conducted on real-world online review datasets, showing that the proposed model outperformed baseline models in effectively predicting user ratings for target items. Furthermore, this study demonstrated the influence of the ABSA method on personalized and explainable recommendations.

Original languageEnglish
Pages (from-to)5781-5791
Number of pages11
JournalIEEE Access
Volume12
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Aspect-based sentiment analysis
  • attention mechanism
  • deep learning
  • online review
  • recommendation model
  • user preference

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