IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v508y2018icp254-264.html
   My bibliography  Save this article

Long-term memory of rating behaviors for the online trust formation

Author

Listed:
  • Guo, Xin-Yu
  • Guo, Qiang
  • Li, Ren-De
  • Liu, Jian-Guo

Abstract

Investigating the dynamics of long-term memory in online rating behaviors is significant for understanding the evolution mechanism of collective behaviors and trust formation for online social networks. Since users are allowed to deliver ratings in many online systems, ratings can well reflect the user’s opinions. In this paper, we empirically investigate the long-term memory, measured by the Detrended Fluctuation Analysis, in collective rating behaviors before and after the trust formation. The results for the Epinions data set show that, comparing with the null model generated by the reshuffle process, the Hurst exponent of trustors (trustees) decreases 7.12% (9.05%) before and increases 7.36% (9.20%) after trust formation, which stably remains close to 0.5 in null model I and 0.6 in null model II, suggesting that the collective rating behavior plays an important role for the trust formation. Furthermore, we divide users into 8 groups according to the user degree and find that the correlation of the user degree and the variation of Hurst exponent, measured by the Pearson Correlation Coefficient, is 0.8629 and 0.8620 before and after trust formation respectively, reflecting a significant correlation between user degrees and collective rating behavior patterns. Finally, we select the users without creating other trust relations around the trust formation time and the result suggests that the collective rating behaviors indeed change for the trust formation. This work helps deeply understand the intrinsic feedback effects between collective behaviors and trust relationship.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:254-264
    DOI: 10.1016/j.physa.2018.05.106
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118306320
    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.2018.05.106?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. Ausloos, M & Ivanova, K, 2000. "Introducing False EUR and False EUR exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 286(1), pages 353-366.
    2. Sunkyu Yu & Xianji Piao & Jiho Hong & Namkyoo Park, 2015. "Bloch-like waves in random-walk potentials based on supersymmetry," Nature Communications, Nature, vol. 6(1), pages 1-8, November.
    3. Da-Cheng Nie & Zi-Ke Zhang & Jun-Lin Zhou & Yan Fu & Kui Zhang, 2014. "Information Filtering on Coupled Social Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-15, July.
    4. Da-Cheng Nie & Ming-Jing Ding & Yan Fu & Jun-Lin Zhou & Zi-Ke Zhang, 2013. "Social Interest For User Selecting Items In Recommender Systems," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 24(04), pages 1-11.
    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. 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.
    7. Guo, Qiang & Song, Wen-Jun & Hou, Lei & Zhang, Yi-Lu & Liu, Jian-Guo, 2014. "Effect of the time window on the heat-conduction information filtering model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 401(C), pages 15-21.
    8. Peng, C.-K. & Hausdorff, J.M. & Havlin, S. & Mietus, J.E. & Stanley, H.E. & Goldberger, A.L., 1998. "Multiple-time scales analysis of physiological time series under neural control," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 249(1), pages 491-500.
    9. Huang, G.C. & Soto, D. & Fujimoto, K. & Valente, T.W., 2014. "The interplay of friendship networks and social networking sites: Longitudinal analysis of selection and influence effects on adolescent smoking and alcohol use," American Journal of Public Health, American Public Health Association, vol. 104(8), pages 51-59.
    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. Farid Makhlouf & Refk Selmi, 2021. "The role of remittances in times of socio-political unrest: Evidence from Tunisia," Working Papers hal-03263815, HAL.

    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. Sidorov, S.P. & Faizliev, A.R. & Balash, V.A. & Korobov, E.A., 2016. "Long-range correlation analysis of economic news flow intensity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 205-212.
    2. Li, Ren-De & Liu, Jian-Guo & Guo, Qiang & Zhang, Yi-Cheng, 2018. "Social signature identification of dynamical social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 213-222.
    3. Hou, Lei & Liu, Kecheng & Liu, Jianguo & Zhang, Runtong, 2017. "Solving the stability–accuracy–diversity dilemma of recommender systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 415-424.
    4. Şahin, Gökhan & Erentürk, Murat & Hacinliyan, Avadis, 2009. "Detrended fluctuation analysis in natural languages using non-corpus parametrization," Chaos, Solitons & Fractals, Elsevier, vol. 41(1), pages 198-205.
    5. 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.
    6. 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.
    7. 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.
    8. Ramezani, Mohsen & Moradi, Parham & Akhlaghian, Fardin, 2014. "A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 408(C), pages 72-84.
    9. Matsushita, Raul & Gleria, Iram & Figueiredo, Annibal & Rathie, Pushpa & Da Silva, Sergio, 2004. "Exponentially damped Lévy flights, multiscaling, and exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 353-369.
    10. 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.
    11. Mariani, M.C. & Florescu, I. & Beccar Varela, M.P. & Ncheuguim, E., 2010. "Study of memory effects in international market indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1653-1664.
    12. 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.
    13. Rosella Castellano & Roy Cerqueti & Giulia Rotundo, 2020. "Exploring the financial risk of a temperature index: a fractional integrated approach," Annals of Operations Research, Springer, vol. 284(1), pages 225-242, January.
    14. 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.
    15. Celeste, Valerio & Corbet, Shaen & Gurdgiev, Constantin, 2020. "Fractal dynamics and wavelet analysis: Deep volatility and return properties of Bitcoin, Ethereum and Ripple," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 310-324.
    16. Saha, Debajyoti & Shaw, Pankaj Kumar & Ghosh, Sabuj & Janaki, M.S. & Sekar Iyengar, A.N., 2018. "Quantification of scaling exponent with Crossover type phenomena for different types of forcing in DC glow discharge plasma," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 300-310.
    17. Farid Makhlouf & Refk Selmi, 2021. "The role of remittances in times of socio-political unrest: Evidence from Tunisia," Working Papers hal-03263815, HAL.
    18. Boilard, J.-F. & Kanazawa, K. & Takayasu, H. & Takayasu, M., 2018. "Empirical scaling relations of market event rates in foreign currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1152-1161.
    19. Siqueira, Erinaldo Leite & Stošić, Tatijana & Bejan, Lucian & Stošić, Borko, 2010. "Correlations and cross-correlations in the Brazilian agrarian commodities and stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2739-2743.
    20. Wang, Yang & Han, Lixin, 2020. "Personalized recommendation via network-based inference with time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).

    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:508:y:2018:i:c:p:254-264. 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.