Journal
CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT
Volume -, Issue -, Pages 1767-1770Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3269206.3269234
Keywords
Recommender Systems; Social Recommendation; Characterized Social Regularization; Matrix Factorization
Funding
- National Key Research and Development Program of China [2017YFE0112300]
- National Nature Science Foundation of China [61861136003, 61621091, 61673237]
- Beijing National Research Center for Information Science and Technology [20031887521]
- Tsinghua University - Tencent Joint Laboratory for Internet Innovation Technology
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Social recommendation, which utilizes social relations to enhance recommender systems, has been gaining increasing attention recently with the rapid development of online social network. Existing social recommendation methods are based on the fact that users preference or decision is influenced by their social friends' behaviors. However, they assume that the influences of social relation are always the same, which violates the fact that users are likely to share preference on diverse products with different friends. In this paper, we present a novel CSR (short for Characterized Social Regularization) model by designing a universal regularization term for modeling variable social influence. Our proposed model can be applied to both explicit and implicit iteration. Extensive experiments on a real-world dataset demonstrate that CSR significantly outperforms state-of-the-art social recommendation methods.
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