영상 소비 데이터를 기반으로 한교차 도메인에서 개인 맞춤형 도서 추천
Personalized Cross-Domain Recommendation of Books Based on Video Consumption Data
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초록
최근 성인 독서량은 지속적으로 감소하는데 비해 영상 콘텐츠 소비가 증가하고 있다. 이에 따라 새로운 사용자에 대한 선호도 및 행동 패턴에대한 정보가 없고 새로운 도서에 대한 사용자 평가나 구매 정보가 부족해 콜드 스타트 문제와 데이터 희소성 문제가 발생하고 있다. 본 논문에서는영상물 콘텐츠 기반 도서 하이브리드 추천 시스템을 제안하였다. 제안하는 추천 시스템은 영상물의 콘텐츠를 활용하여 콜드 스타트 문제와 데이터희소성 문제를 해결할 수 있을 뿐만 아니라, 전통적인 도서 추천 시스템에 비해 성능이 향상됨을 보여주었다. 또한 장르, 줄거리, 평점 정보 등사용자 취향 정보까지 모두 반영한 개인 맞춤형 추천 결과를 제시하였다.
Abstract
Recently, the amount of adult reading has been continuously decreasing, but the consumption of video content is increasing. Accordingly, there is no information on preferences and behavior patterns for new users, and user evaluation or purchase of newbooks are insufficient, causing cold start problems and data scarcity problems. In this paper, a hybrid book recommendation systembased on video content was proposed. The proposed recommendation system can not only solve the cold start problem and data scarcityproblem by utilizing the contents of the video, but also has improved performance compared to the traditional book recommendationsystem, and even high-quality recommendation results that reflect genre, plot, and rating information-based user taste informationwere confirmed.
참고문헌 (11)
- "The book-covered south korea, how about you?... 53% of adults haven't read a single book in a year" (2022)[URL]
- Ministry of Culture, "Sports and Tourism", pp.12~14 (2022), Cultural policy
- T-H. Kim, "SVD-based cross-domain recommendation using k-means clustering", Journal of KIISE, 49(5), pp.360~368 (2022)
- Y. Koren, "Matrix factorization techniques for recommender systems", Journal of Com"puter, 42(8), pp.30~37 (2009)
- D. Goldberg, "Using collaborative filtering to weave an information tapestry", Communications of the ACM, 35(12), pp.61~70 (1992)
- B. G. Sarwar, "Item-based collaborative filtering recommendation algorithms", Proceedings of the 10th International Conference on World Wide Web, pp.285~295 (2001)
- J. Hyun, "How to improve the accuracy of recommendation systems : Combining ratings and review texts sentiment score", Journal of Intelligence and Information Systems, 25(1), pp.219~239 (2019)
- S. Kim, "Cross media platform book recommender system : Based on book and movie ratings", Journal of the Convergence on Culture Technology(JCCT), 7(1), pp.582~587 (2021)
- M. J. Ku, "A hybrid recommender system based on collaborative filtering with selective use of overall and multicriteria rating", Journal of Intelligence and Information Systems, 24(2), pp.85~109 (2018)
- J. Yoon, "A hybrid recommendation method in cross domain based on user preferences and content information", Journal of Korean Institute of Intelligent Systems, 32(6), pp.486~493 (2022)
- K. Järvelin, "IR evaluation methods for retrieving highly relevant documents", Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '00). Association for Computing Machinery, pp.41~48 (2000)