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Alleviating the recommendation bias via rank aggregation

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  • Dong, Qiang
  • Yuan, Quan
  • Shi, Yang-Bo

Abstract

The primary goal of a recommender system is often known as “helping users find relevant items”, and a lot of recommendation algorithms are proposed accordingly. However, these accuracy-oriented methods usually suffer the problem of recommendation bias on popular items, which is not welcome to not only users but also item providers. To alleviate the recommendation bias problem, we propose a generic rank aggregation framework for the recommendation results of an existing algorithm, in which the user- and item-oriented ranking results are linearly aggregated together, with a parameter controlling the weight of the latter ranking process. Experiment results of a typical algorithm on two real-world data sets show that, this framework is effective to improve the recommendation fairness of any existing accuracy-oriented algorithms, while avoiding significant accuracy loss.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:phsmap:v:534:y:2019:i:c:s0378437119312051
    DOI: 10.1016/j.physa.2019.122073
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    References listed on IDEAS

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    1. Wen-Jun Li & Yuan-Yuan Xu & Qiang Dong & Jun-Lin Zhou & Yan Fu, 2015. "TaDb: A time-aware diffusion-based recommender algorithm," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 26(09), pages 1-12.
    2. 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.
    3. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    4. Zhao, Yao-Dong & Cai, Shi-Min & Tang, Ming & Shang, Min-Sheng, 2017. "Coarse cluster enhancing collaborative recommendation for social network systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 209-218.
    5. Zhu, Xuzhen & Tian, Hui & Chen, Guilin & Cai, Shimin, 2017. "Symmetrical and overloaded effect of diffusion in information filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 9-15.
    6. 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.
    7. 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.
    8. Zhu, Xuzhen & Tian, Hui & Zhang, Tianqiao, 2018. "Symmetrical information filtering via punishing superfluous diffusion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 1-9.
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