DEGREE CORRELATION OF BIPARTITE NETWORK ON PERSONALIZED RECOMMENDATION

Jian-Guo Liu (), Tao Zhou (), Bing-Hong Wang, Yi-Cheng Zhang and Qiang Guo
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Jian-Guo Liu: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
Tao Zhou: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
Bing-Hong Wang: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
Yi-Cheng Zhang: Research Center of Complex Systems Science, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
Qiang Guo: Business School, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China

International Journal of Modern Physics C (IJMPC), 2010, vol. 21, issue 01, 137-147

Abstract: In this paper, the statistical property, namely degree correlation between users and objects, is taken into account and be embedded into the similarity index of collaborative filtering (CF) algorithm to improve the algorithmic performance. The numerical simulation on a benchmark data set shows that the algorithmic accuracy of the presented algorithm, measured by the average ranking score, is improved by 18.19% in the optimal case. The statistical analysis on the product distribution of the user and object degrees indicate that, in the optimal case, the distribution obeys the power-law and the exponential is equal to -2.33. Numerical results show that the presented algorithm can provide more diverse and less popular recommendations, for example, when the recommendation list contains 10 objects, the diversity, measured by the hamming distance, is improved by 21.90%. Since all of the real recommendation data evolving with time, this work may shed some light on the adaptive recommendation algorithm which could change its parameter automatically according to the statistical properties of the user-object bipartite network.

Keywords: Recommendation systems; bipartite network; collaborative filtering; 89.75.Hc; 87.23.Ge; 05.70.Ln (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183110014999

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