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Multicriteria Movie Recommendation Model Combining Aspect-based Sentiment Classification Using BERT

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(3), pp.201-207
  • DOI : 10.9708/jksci.2022.27.03.201
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : February 21, 2022
  • Accepted : March 23, 2022
  • Published : March 31, 2022

Yurin Lee 1 Ahn, Hyunchul 1

1국민대학교

Accredited

ABSTRACT

In this paper, we propose a movie recommendation model that uses the users’ ratings as well as their reviews. To understand the user’s preference from multicriteria perspectives, the proposed model is designed to apply attribute-based sentiment analysis to the reviews. For doing this, it divides the reviews left by customers into multicriteria components according to its implicit attributes, and applies BERT-based sentiment analysis to each of them. After that, our model selectively combines the attributes that each user considers important to CF to generate recommendation results. To validate usefulness of the proposed model, we applied it to the real-world movie recommendation case. Experimental results showed that the accuracy of the proposed model was improved compared to the traditional CF. This study has academic and practical significance since it presents a new approach to select and use models in consideration of individual characteristics, and to derive various attributes from a review instead of evaluating each of them.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.