IMPROVED COLLABORATIVE FILTERING ALGORITHM VIA INFORMATION TRANSFORMATION

Jian-Guo Liu (), Bing-Hong Wang () and Qiang Guo ()
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Jian-Guo Liu: Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, P. R. China;
Bing-Hong Wang: Department of Modern Physics and Nonlinear Science Center, University of Science and Technology of China, Hefei 230026, P. R. China;
Qiang Guo: Dalian Nationalities University, Dalian 116600, P. R. China

International Journal of Modern Physics C (IJMPC), 2009, vol. 20, issue 02, 285-293

Abstract: In this paper, we propose a spreading activation approach for collaborative filtering (SA-CF). By using the opinion spreading process, the similarity between any users can be obtained. The algorithm has remarkably higher accuracy than the standard collaborative filtering using the Pearson correlation. Furthermore, we introduce a free parameter β to regulate the contributions of objects to user–user correlations. The numerical results indicate that decreasing the influence of popular objects can further improve the algorithmic accuracy and personality. We argue that a better algorithm should simultaneously require less computation and generate higher accuracy. Accordingly, we further propose an algorithm involving only the top-Nsimilar neighbors for each target user, which has both less computational complexity and higher algorithmic accuracy.

Keywords: Recommendation systems; bipartite network; collaborative filtering; 89.75.Hc; 87.23.Ge; 05.70.Ln (search for similar items in EconPapers)
Date: 2009
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1142/S0129183109013613

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