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Ceiling effect of online user interests for the movies

Author

Listed:
  • Ni, Jing
  • Zhang, Yi-Lu
  • Hu, Zhao-Long
  • Song, Wen-Jun
  • Hou, Lei
  • Guo, Qiang
  • Liu, Jian-Guo

Abstract

Online users’ collective interests play an important role for analyzing the online social networks and personalized recommendations. In this paper, we introduce the information entropy to measure the diversity of the user interests. We empirically analyze the information entropy of the objects selected by the users with the same degree in both the MovieLens and Netflix datasets. The results show that as the user degree increases, the entropy increases from the lowest value at first to the highest value and then begins to fall, which indicates that the interests of the small-degree and large-degree users are more centralized, while the interests of normal users are more diverse. Furthermore, a null model is proposed to compare with the empirical results. In a null model, we keep the number of users and objects as well as the user degrees unchangeable, but the selection behaviors are totally random in both datasets. Results show that the diversity of the majority of users in the real datasets is higher than that the random case, with the exception of the diversity of only a fraction of small-degree users. That may because new users just like popular objects, while with the increase of the user experiences, they quickly become users of broad interests. Therefore, small-degree users’ interests are much easier to predict than the other users’, which may shed some light for the cold-start problem.

Suggested Citation

  • Ni, Jing & Zhang, Yi-Lu & Hu, Zhao-Long & Song, Wen-Jun & Hou, Lei & Guo, Qiang & Liu, Jian-Guo, 2014. "Ceiling effect of online user interests for the movies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 134-140.
    • Handle: RePEc:eee:phsmap:v:402:y:2014:i:c:p:134-140
    DOI: 10.1016/j.physa.2014.01.046
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. Zhang, Yi-Lu & Guo, Qiang & Ni, Jing & Liu, Jian-Guo, 2015. "Memory effect of the online rating for movies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 261-266.
    2. Li, Sheng-Nan & Guo, Qiang & Yang, Kai & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Uncovering the popularity mechanisms for Facebook applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 422-429.
    3. Leilei Wu & Zhuoming Ren & Xiao-Long Ren & Jianlin Zhang & Linyuan Lü, 2018. "Eliminating the Effect of Rating Bias on Reputation Systems," Complexity, Hindawi, vol. 2018, pages 1-11, February.
    4. Wang, Jia-Hua & Guo, Qiang & Yang, Kai & Zhang, Yi-Lu & Han, Jingti & Liu, Jian-Guo, 2016. "Popularity and user diversity of online objects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 480-486.
    5. Lei Ji & Jian-Guo Liu & Lei Hou & Qiang Guo, 2015. "Identifying the Role of Common Interests in Online User Trust Formation," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-15, July.
    6. Dai, Lu & Guo, Qiang & Liu, Xiao-Lu & Liu, Jian-Guo & Zhang, Yi-Cheng, 2018. "Identifying online user reputation in terms of user preference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 403-409.
    7. Wang, Ximeng & Liu, Yun & Xiong, Fei, 2016. "Improved personalized recommendation based on a similarity network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 271-280.
    8. Liu, Xiao-Lu & Guo, Qiang & Hou, Lei & Cheng, Can & Liu, Jian-Guo, 2015. "Ranking online quality and reputation via the user activity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 629-636.
    9. Liu, Xiao-Lu & Liu, Jian-Guo & Yang, Kai & Guo, Qiang & Han, Jing-Ti, 2017. "Identifying online user reputation of user–object bipartite networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 508-516.

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