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Diffusion-like recommendation with enhanced similarity of objects

Author

Listed:
  • An, Ya-Hui
  • Dong, Qiang
  • Sun, Chong-Jing
  • Nie, Da-Cheng
  • Fu, Yan

Abstract

In the last decade, diversity and accuracy have been regarded as two important measures in evaluating a recommendation model. However, a clear concern is that a model focusing excessively on one measure will put the other one at risk, thus it is not easy to greatly improve diversity and accuracy simultaneously. In this paper, we propose to enhance the Resource-Allocation (RA) similarity in resource transfer equations of diffusion-like models, by giving a tunable exponent to the RA similarity, and traversing the value of this exponent to achieve the optimal recommendation results. In this way, we can increase the recommendation scores (allocated resource) of many unpopular objects. Experiments on three benchmark data sets, MovieLens, Netflix and RateYourMusic show that the modified models can yield remarkable performance improvement compared with the original ones.

Suggested Citation

  • An, Ya-Hui & Dong, Qiang & Sun, Chong-Jing & Nie, Da-Cheng & Fu, Yan, 2016. "Diffusion-like recommendation with enhanced similarity of objects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 708-715.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:708-715
    DOI: 10.1016/j.physa.2016.06.027
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    References listed on IDEAS

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    1. Da-Cheng Nie & Zi-Ke Zhang & Jun-Lin Zhou & Yan Fu & Kui Zhang, 2014. "Information Filtering on Coupled Social Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-15, July.
    2. Jin-Hu Liu & Tao Zhou & Zi-Ke Zhang & Zimo Yang & Chuang Liu & Wei-Min Li, 2014. "Promoting Cold-Start Items in Recommender Systems," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.
    3. Nie, Da-Cheng & An, Ya-Hui & Dong, Qiang & Fu, Yan & Zhou, Tao, 2015. "Information filtering via balanced diffusion on bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 44-53.
    4. Zhang, Zi-Ke & Zhou, Tao & Zhang, Yi-Cheng, 2010. "Personalized recommendation via integrated diffusion on user–item–tag tripartite graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 179-186.
    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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    Cited by:

    1. Chen, Guilin & Gao, Tianrun & Zhu, Xuzhen & Tian, Hui & Yang, Zhao, 2017. "Personalized recommendation based on preferential bidirectional mass diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 397-404.
    2. Latha, R., 2022. "Enhancing recommendation competence in nearest neighbour models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    3. Dong, Qiang & Yuan, Quan & Shi, Yang-Bo, 2019. "Alleviating the recommendation bias via rank aggregation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

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