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
- 2012.5
- 수록면
- 95 - 102 (8page)
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초록· 키워드
오류제보하기
Various recommendation algorithms have been developed to improve the accuracy of recommendations; however, the diversity of recommendations has often been overlooked. Intuitively, it may be possible to achieve improvements in one of these two metrics at the expense of the other. For example, higher accuracy may sometimes be obtained by safely recommending to users the most popular items, which can lead to the reduction in aggregate recommendation diversity, i.e., less personalized recommendations. Conversely, higher diversity can be achieved by trying to uncover and recommend highly personalized items for each user, which are inherently more difficult to predict and, thus, may lead to a decrease in recommendation accuracy. To overcome this accuracy-diversity tradeoff, this work builds on the following two ideas: incorporating multi-criteria rating information and applying different ranking methods. Experiments using movie rating data empirically demonstrate simultaneous improvements in accuracy and diversity.
목차
Abstract
Introduction and Motivation
Related Work
New Approaches For Accuracy and Diversity
Empirical Results
Conclusions and Future Work
Acknowledgments
References
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