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Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm

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  • Liu, Chuang
  • Zhou, Wei-Xing

Abstract

Network-based recommendation algorithms for user–object link predictions have achieved significant developments in recent years. For bipartite graphs, the resource reallocation in such algorithms is analogous to heat spreading (HeatS) or probability spreading (ProbS) processes. The best algorithm to date is a hybrid of the HeatS and ProbS techniques with homogeneous initial resource configurations, which fulfills simultaneously high accuracy and large diversity requirements. We investigate the effect of heterogeneity in initial configurations on the HeatS + ProbS hybrid algorithm and find that both recommendation accuracy and diversity can be further improved in this new setting. Numerical experiments show that the improvement is robust.

Suggested Citation

  • Liu, Chuang & Zhou, Wei-Xing, 2012. "Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5704-5711.
  • Handle: RePEc:eee:phsmap:v:391:y:2012:i:22:p:5704-5711
    DOI: 10.1016/j.physa.2012.06.034
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    References listed on IDEAS

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    1. Liu, Ji & Deng, Guishi, 2009. "Link prediction in a user–object network based on time-weighted resource allocation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3643-3650.
    2. Lü, Linyuan & Zhou, Tao, 2011. "Link prediction in complex networks: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1150-1170.
    3. Zhang, Cheng-Jun & Zeng, An, 2012. "Behavior patterns of online users and the effect on information filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1822-1830.
    4. Jia, Chun-Xiao & Liu, Run-Ran & Sun, Duo & Wang, Bing-Hong, 2008. "A new weighting method in network-based recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(23), pages 5887-5891.
    5. Tao Zhou & Linyuan Lü & Yi-Cheng Zhang, 2009. "Predicting missing links via local information," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 71(4), pages 623-630, October.
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    Citations

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    Cited by:

    1. Song, Wen-Jun & Guo, Qiang & Liu, Jian-Guo, 2014. "Improved hybrid information filtering based on limited time window," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 192-197.
    2. Wen, Yuan & Liu, Yun & Zhang, Zhen-Jiang & Xiong, Fei & Cao, Wei, 2014. "Compare two community-based personalized information recommendation algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 199-209.
    3. Zhang, Yin & Gao, Kening & Zhang, Bin, 2015. "The concept exploration model and an application," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 430-442.
    4. Yu, Fei & Zeng, An & Gillard, Sébastien & Medo, Matúš, 2016. "Network-based recommendation algorithms: A review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 192-208.

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