한국IT서비스학회지 (Journal of Information Technology Services)

한국IT서비스학회 (Korea Society of IT Services)
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설명 가능한 개인화 영화 추천 서비스를 위한 딥러닝 기반 텍스트 요약 모델

Deep Learning-based Text Summarization Model for Explainable Personalized Movie Recommendation Service

  • 진요요 (경희대학교 대학원 경영학과) ;
  • 강경모 (경희대학교 대학원 빅데이터응용학과) ;
  • 김재경 (경희대학교 경영대학 & 대학원 빅데이터응용학과)
  • 투고 : 2022.01.20
  • 심사 : 2022.03.27
  • 발행 : 2022.04.30
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초록

The number and variety of products and services offered by companies have increased dramatically, providing customers with more choices to meet their needs. As a solution to this information overload problem, the provision of tailored services to individuals has become increasingly important, and the personalized recommender systems have been widely studied and used in both academia and industry. Existing recommender systems face important problems in practical applications. The most important problem is that it cannot clearly explain why it recommends these products. In recent years, some researchers have found that the explanation of recommender systems may be very useful. As a result, users are generally increasing conversion rates, satisfaction, and trust in the recommender system if it is explained why those particular items are recommended. Therefore, this study presents a methodology of providing an explanatory function of a recommender system using a review text left by a user. The basic idea is not to use all of the user's reviews, but to provide them in a summarized form using only reviews left by similar users or neighbors involved in recommending the item as an explanation when providing the recommended item to the user. To achieve this research goal, this study aims to provide a product recommendation list using user-based collaborative filtering techniques, combine reviews left by neighboring users with each product to build a model that combines text summary methods among deep learning-based natural language processing methods. Using the IMDb movie database, text reviews of all target user neighbors' movies are collected and summarized to present descriptions of recommended movies. There are several text summary methods, but this study aims to evaluate whether the review summary is well performed by training the Sequence-to-sequence+attention model, which is a representative generation summary method, and the BertSum model, which is an extraction summary model.

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과제정보

본 논문은 교육부 및 한국연구재단의 4단계 두뇌한국21 사업(4단계 BK21 사업)으로 지원된 연구임. 이 논문 또는 저서는 2020년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임(NRF-2020S1A5B8103855)

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