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
- 2013.11
- 수록면
- 196 - 201 (6page)
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초록· 키워드
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Personalization aims for providing customized contents to each user by using their personal preferences. In this sense, the core parts of personalization are regarded as recommendation technologies which can recommend proper contents or products to each user according to each user’s preference. Prior studies have proposed novel recommendation technologies because they recognized the importance of recommender systems. Among several recommendation technologies, collaborative filtering (CF) have been actively studied and applied in real-world. The CF, however, often suffer sparsity or scalability problems. Prior research also recognized the importance of these two problems and proposed many solutions for them. Many prior studies, however, had problems that it needed additional time and cost for solving the limitations by using additional information from other sources besides the existing user-item matrix. This study proposes novel implicit rating approach for collaborative filtering to mitigate the sparsity problem and to enhance the performance of recommender systems. In this study, we propose the methods of reducing the sparsity problem through the supplement of the user-item matrix based on implicit trust score which measures the trust level among users by using the existing user-item matrix. This study provides preliminary experimental results to test usefulness of the proposed model.
목차
Abstract
Introduction
Prior Researches
Implicit Trust-based Filtering
Experiments
Conclusions
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UCI(KEPA) : I410-ECN-0101-2015-003-001311542