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
- 학술대회자료
- 저자정보
- 발행연도
- 2014.11
- 수록면
- 137 - 143 (7page)
이용수
초록· 키워드
오류제보하기
Collaborative filtering (CF) is one the most popular core methods used in recommender system to provide user a personalized service. However, cold-start problem which happens when new users or items get to the system and do not have any ratings history, presents a serious problem to recommendation system which lead to the lack of accuracy in prediction. In this paper, we propose a novel approach that integrates user preferences extracted from social network with traditional CF to overcome this problem and improve performance of CF. We first cluster user based on genre and production company interests. Then we make predictions by combining the average ratings within the user groups with traditional CF using a coefficient. We conduct an experiment on real dataset in a movie recommendation system and Facebook, and the results show the much improvements of our approach over the traditional CF algorithm in dealing with user cold-start problem.
목차
Abstract
1. Introduction
2. Related Works
3. Clustering Method Based on Personal Preferences Method
4. Experimental Evaluation and Results
5. Conclusion and Future Work
References
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UCI(KEPA) : I410-ECN-0101-2016-003-001368023