IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v417y2015icp261-266.html
   My bibliography  Save this article

Memory effect of the online rating for movies

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437114007705
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2014.09.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gu, Ke & Fan, Ying & Di, Zengru, 2020. "How to predict recommendation lists that users do not like," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    2. 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.
    3. 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.
    4. Fan, Rui & Xu, Ke & Zhao, Jichang, 2018. "An agent-based model for emotion contagion and competition in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 245-259.
    5. 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.
    6. Jong-Seok Lee & Dan Zhu, 2012. "Shilling Attack Detection---A New Approach for a Trustworthy Recommender System," INFORMS Journal on Computing, INFORMS, vol. 24(1), pages 117-131, February.
    7. Liu, Chuang & Zhou, Wei-Xing, 2012. "Heterogeneity in initial resource configurations improves a network-based hybrid recommendation algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5704-5711.
    8. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2021. "Tensorial graph learning for link prediction in generalized heterogeneous networks," European Journal of Operational Research, Elsevier, vol. 290(1), pages 219-234.
    9. Zan Huang & Daniel Dajun Zeng, 2011. "Why Does Collaborative Filtering Work? Transaction-Based Recommendation Model Validation and Selection by Analyzing Bipartite Random Graphs," INFORMS Journal on Computing, INFORMS, vol. 23(1), pages 138-152, February.
    10. Duan, Boping & Liu, Jing & Zhou, Mingxing & Ma, Liangliang, 2016. "A comparative analysis of network robustness against different link attacks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 144-153.
    11. Laureti, Paolo & Moret, Lionel & Zhang, Yi-Cheng, 2005. "Aggregating partial, local evaluations to achieve global ranking," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(3), pages 705-712.
    12. 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.
    13. 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.
    14. Chen, Ling-Jiao & Gao, Jian, 2018. "A trust-based recommendation method using network diffusion processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 679-691.
    15. 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.
    16. Hu, Liang & Ren, Liang & Lin, Wenbin, 2018. "A reconsideration of negative ratings for network-based recommendation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 690-701.
    17. Christian Matt & Thomas Hess, 2016. "Product fit uncertainty and its effects on vendor choice: an experimental study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(1), pages 83-93, February.
    18. Yin, Chun-Xia & Peng, Qin-Ke & Chu, Tao, 2012. "Personal artist recommendation via a listening and trust preference network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(5), pages 1991-1999.
    19. Joonhyuk Yang & Jinwook Kim & Wonjoon Kim & Young Hwan Kim, 2012. "Measuring User Similarity Using Electric Circuit Analysis: Application to Collaborative Filtering," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-10, November.
    20. Gediminas Adomavicius & YoungOk Kwon, 2014. "Optimization-Based Approaches for Maximizing Aggregate Recommendation Diversity," INFORMS Journal on Computing, INFORMS, vol. 26(2), pages 351-369, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:417:y:2015:i:c:p:261-266. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.