지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- Korean Institute of Information Scientists and Engineers Journal of Computing Science and Engineering Journal of Computing Science and Engineering Vol.7 No.1
- 발행연도
- 2013.3
- 수록면
- 21 - 29 (9page)
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초록· 키워드
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The introduction of recommender systems to existing online services is now practically inevitable, with the increasing number of items and users on online services. Popular recommender systems have successfully implemented satisfactory systems, which are usually based on collaborative filtering. However, collaborative filtering-based recommenders suffer from well-known problems, such as popularity bias, and the cold-start problem. In this paper, we propose an innovative collaborative-filtering based recommender system, which uses the concepts of Experts and Novices to create finegrained recommendations that focus on being novel, while being kept relevant. Experts and Novices are defined using pre-made clusters of similar items, and the distribution of users’ ratings among these clusters. Thus, in order to generate recommendations, the experts are found dynamically depending on the seed items of the novice. The proposed recommender system was built using the MovieLens 1 M dataset, and evaluated with novelty metrics. Results show that the proposed system outperforms matrix factorization methods according to discovery-based novelty metrics, and can be a solution to popularity bias and the cold-start problem, while still retaining collaborative filtering.
목차
Abstract
Ⅰ. INTRODUCTION
Ⅱ. RELATED WORK
Ⅲ. CONCEPT
Ⅳ. DYNAMICALLY PROMOTED EXPERT-BASED RECOMMENDER SYSTEM
Ⅴ. EXPERIMENT
Ⅵ. EVALUATION OF NOVELTY IN RECOMMENDER SYSTEMS
Ⅶ. RESULTS AND DISCUSSION
Ⅷ. CONCLUSION AND FUTURE WORK
REFERENCES
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UCI(KEPA) : I410-ECN-0101-2014-560-003150163