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Long-term effects of user preference-oriented recommendation method on the evolution of online system

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  • Shi, Xiaoyu
  • Shang, Ming-Sheng
  • Luo, Xin
  • Khushnood, Abbas
  • Li, Jian

Abstract

As the explosion growth of Internet economy, recommender system has become an important technology to solve the problem of information overload. However, recommenders are not one-size-fits-all, different recommenders have different virtues, making them be suitable for different users. In this paper, we propose a novel personalized recommender based on user preferences, which allows multiple recommenders to exist in E-commerce system simultaneously. We find that output of a recommender to each user is quite different when using different recommenders, the recommendation accuracy can be significantly improved if each user is assigned with his/her optimal personalized recommender. Furthermore, different from previous works focusing on short-term effects on recommender, we also evaluate the long-term effect of the proposed method by modeling the evolution of mutual feedback between user and online system. Finally, compared with single recommender running on the online system, the proposed method can improve the accuracy of recommendation significantly and get better trade-offs between short- and long-term performances of recommendation.

Suggested Citation

  • Shi, Xiaoyu & Shang, Ming-Sheng & Luo, Xin & Khushnood, Abbas & Li, Jian, 2017. "Long-term effects of user preference-oriented recommendation method on the evolution of online system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 490-498.
    • Handle: RePEc:eee:phsmap:v:467:y:2017:i:c:p:490-498
    DOI: 10.1016/j.physa.2016.10.033
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    References listed on IDEAS

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    1. Liu, Jian-Guo & Zhou, Tao & Che, Hong-An & Wang, Bing-Hong & Zhang, Yi-Cheng, 2010. "Effects of high-order correlations on personalized recommendations for bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(4), pages 881-886.
    2. Zan Huang & Daniel D. Zeng & Hsinchun Chen, 2007. "Analyzing Consumer-Product Graphs: Empirical Findings and Applications in Recommender Systems," Management Science, INFORMS, vol. 53(7), pages 1146-1164, July.
    3. Lieven Wittebolle & Massimo Marzorati & Lieven Clement & Annalisa Balloi & Daniele Daffonchio & Kim Heylen & Paul De Vos & Willy Verstraete & Nico Boon, 2009. "Initial community evenness favours functionality under selective stress," Nature, Nature, vol. 458(7238), pages 623-626, April.
    4. Guan, Yuan & Zhao, Dandan & Zeng, An & Shang, Ming-Sheng, 2013. "Preference of online users and personalized recommendations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3417-3423.
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    Cited by:

    1. Yin, Likang & Deng, Yong, 2018. "Measuring transferring similarity via local information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 498(C), pages 102-115.

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