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Effect of recent popularity on heat-conduction based recommendation models

Author

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  • Li, Wen-Jun
  • Dong, Qiang
  • Shi, Yang-Bo
  • Fu, Yan
  • He, Jia-Lin

Abstract

Accuracy and diversity are two important measures in evaluating the performance of recommender systems. It has been demonstrated that the recommendation model inspired by the heat conduction process has high diversity yet low accuracy. Many variants have been introduced to improve the accuracy while keeping high diversity, most of which regard the current node-degree of an item as its popularity. However in this way, a few outdated items of large degree may be recommended to an enormous number of users. In this paper, we take the recent popularity (recently increased item degrees) into account in the heat-conduction based methods, and propose accordingly the improved recommendation models. Experimental results on two benchmark data sets show that the accuracy can be largely improved while keeping the high diversity compared with the original models.

Suggested Citation

  • Li, Wen-Jun & Dong, Qiang & Shi, Yang-Bo & Fu, Yan & He, Jia-Lin, 2017. "Effect of recent popularity on heat-conduction based recommendation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 334-343.
    • Handle: RePEc:eee:phsmap:v:474:y:2017:i:c:p:334-343
    DOI: 10.1016/j.physa.2017.01.042
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    References listed on IDEAS

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    1. Yanbo Zhou & Linyuan Lü & Weiping Liu & Jianlin Zhang, 2013. "The Power of Ground User in Recommender Systems," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-11, August.
    2. 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.
    3. An Zeng & Stanislao Gualdi & Matúš Medo & Yi-Cheng Zhang, 2013. "Trend Prediction In Temporal Bipartite Networks: The Case Of Movielens, Netflix, And Digg," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 16(04n05), pages 1-15.
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    Cited by:

    1. Wang, Yang & Han, Lixin, 2020. "Personalized recommendation via network-based inference with time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).

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