期刊
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
卷 5, 期 3, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2542048
关键词
Algorithms; Performance; Experimentation; Recommender systems; collaborative filtering; ranking-based collaborative filtering; vector space model; term weighting
资金
- Natural Science Foundation of China [U1201258, 61272240, 71171122]
- National Science Foundation [OCI-1062439, CNS-1058724]
- Humanity and Social Science Foundation of Ministry of Education of China [12YJC630211]
- Specialized Research Foundation of Ministry of Education of China
- Shandong Natural Science Funds for Distinguished Young Scholars [JQ201316]
- Natural Science Foundation of Shandong Province of China [2012BSB01550]
- Specialized Research Foundation of Jinan [20120201]
Collaborative filtering (CF) is an effective technique addressing the information overload problem. CF approaches generally fall into two categories: rating based and ranking based. The former makes recommendations based on historical rating scores of items and the latter based on their rankings. Ranking-based CF has demonstrated advantages in recommendation accuracy, being able to capture the preference similarity between users even if their rating scores differ significantly. In this study, we propose VSRank, a novel framework that seeks accuracy improvement of ranking-based CF through adaptation of the vector space model. In VSRank, we consider each user as a document and his or her pairwise relative preferences as terms. We then use a novel degree-specialty weighting scheme resembling TF-IDF to weight the terms. Extensive experiments on benchmarks in comparison with the state-of-the-art approaches demonstrate the promise of our approach.
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