설명 가능한 개인화 영화 추천 서비스를 위한 딥러닝 기반 텍스트 요약 모델

• 영문명: Deep Learning-based Text Summarization Model for Explainable Personalized Movie Recommendation Service
• 발행기관: 한국IT서비스학회
• 저자명: 진요요(Biyao Chen) 강경모(KyoungMo Kang) 김재경(JaeKyeong Kim)
• 간행물 정보: 『한국IT서비스학회지』제21권 제2호, 109~126쪽, 전체 18쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2022.04.30

영문 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.

목차

1. 서 론

2. 연구 배경

3. 딥러닝 기반 추천 설명 프로세스

4. 학습 및 실험 결과

5. 결 론

참고문헌

키워드

Recommender System Collaborative Filtering Natural Language Processing Text Summarization