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Memory effect of the online rating for movies

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  • Zhang, Yi-Lu
  • Guo, Qiang
  • Ni, Jing
  • Liu, Jian-Guo

Abstract

Online rating can directly reflect users’ collective behavioral patterns which is of great concern in online social systems. In this paper, we investigate the correlations between the users’ rating behaviors and the real-time updated average ratings of objects given from other users’ previous ratings. We average all the ratings rated after the real-time displayed average ratings at a given interval after dividing the data into five groups according to the user degrees. By analyzing two real systems, the results show that in general there is a linear correlation with slope one between them when the displayed average ratings are between 2.0 and 4.5, but users rate higher scores if the displayed average ratings are lower than 2.0, and give lower ratings if the average ratings are higher than 4.5. Besides, small-degree users would rate higher than the real-time displayed average ratings, while large-degree users are stricter with their ratings than the others so that they usually give lower ratings whatever the movies are. Furthermore, the distributions of the users’ rating bias in all the five groups show that the rating biases of the large-degree users are small, yet those of the small-degree users are relatively large. Our findings could be helpful to analyze online users’ collective behaviors as well as abnormal behaviors in the networks.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:417:y:2015:i:c:p:261-266
    DOI: 10.1016/j.physa.2014.09.012
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    References listed on IDEAS

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    1. 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.
    2. Bianconi, G. & Laureti, P. & Yu, Y.-K. & Zhang, Y.-C., 2004. "Ecology of active and passive players and their impact on information selection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 332(C), pages 519-532.
    3. Zhang, Cheng-Jun & Zeng, An, 2012. "Behavior patterns of online users and the effect on information filtering," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1822-1830.
    4. 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. 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.
    2. 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.
    3. Guo, Xin-Yu & Guo, Qiang & Li, Ren-De & Liu, Jian-Guo, 2018. "Long-term memory of rating behaviors for the online trust formation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 254-264.
    4. 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.
    5. Guo, Qiang & Ji, Lei & Liu, Jian-Guo & Han, Jingti, 2017. "Evolution properties of online user preference diversity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 698-713.

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