4.1 Article

Similarity of user ratings play a positive information filter in social networks

Journal

JOURNAL OF THE KOREAN PHYSICAL SOCIETY
Volume 62, Issue 9, Pages 1221-1226

Publisher

KOREAN PHYSICAL SOC
DOI: 10.3938/jkps.62.1221

Keywords

Social networks; Collaborative filtering; Similarity measure; Singular value decomposition

Funding

  1. Major National S&T Program of China [2011ZX03005-002]
  2. National Natural Science Foundation of China [61003234, 60803150]

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In the past decade, social recommending systems have attracted increasing attention from the physical, social and computer science communities. In this study, we use social networks to capture similarities of users' interest and, accordingly, recommending systems to explore latent similarities. We build similarity-ratings-prediction models for a dataset of books and reviewers from Douban.com. The similarities are measured by the Pearson and the Jaccard correlation coefficients. Using singular value decomposition, we evaluate the strengths and the weaknesses of the similarity measure, and discuss their effectiveness in recommending systems.

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