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The Influence of Interaction Attributes on Trust in Virtual Communities

  • Lizi Zhang
  • Cheun Pin Tan
  • Siyi Li
  • Hui Fang
  • Pramodh Rai
  • Yao Chen
  • Rohit Luthra
  • Wee Keong Ng
  • Jie Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7138)

Abstract

In virtual communities (e.g., forums, blogs), modeling the trust of community members is an effective way to help members make decisions about whose information is more valuable. Towards this goal, we first formulate hypotheses on how various interaction attributes influence trust in virtual communities, and validate these hypotheses through experiments on real data. The influential attributes are then used to develop a trust ranking-based recommendation model called TruRank for recommending the most trustworthy community members. Contrary to traditional recommender systems that rely heavily on subjective manual feedback, our model is built on the foundation of carefully verified objective interaction attributes in virtual communities.

Keywords

Trust reputation system recommender system virtual community 

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References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Transaction on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Brennan, M., Wrazien, S., Greenstadt, R.: Using machine learning to augment collaborative filtering of community discussions. In: 9th International Conference on Autonomous Agents and Multiagent Systems (2010)Google Scholar
  3. 3.
    Chen, C., Hung, S.: To give or to receive? factors influencing members’ knowledge sharing and community promotion in professional virtual communities. Information and Management 47(4) (2010)Google Scholar
  4. 4.
    Cheng, Z., Hurley, N.: Analysis of robustness in trust-based recommender systems. In: 9th RIAO Conference Adaptivity, Personalization and Fusion of Heterogeneous Information (2010)Google Scholar
  5. 5.
    Golbeck, J.: Computing and applying trust in web-based social networks. PhD thesis, University of Maryland College Park (2005)Google Scholar
  6. 6.
    Grandison, T., Sloman, M.: A survey of trust in internet applications. IEEE Communications Surveys and Tutorials 3 (2000)Google Scholar
  7. 7.
    Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: International Conference on Knowledge Discovery and Data Mining (2009)Google Scholar
  8. 8.
    Jøsang, A., Ismail, R., Boyd, C.: A survey of trust and reputation systems for online service provision. Decision Support Systems 43(2), 618–644 (2007)CrossRefGoogle Scholar
  9. 9.
    Kaltenbrunner, A., Gómez, V., López, V.: Description and prediction of slashdot activity. In: 5th Latin American Web Congress (2007)Google Scholar
  10. 10.
    Kaltenbrunner, A., Gómez, V., Moghnieh, A., Meza, R., Blat, J., López, V.: Homogeneous temporal activity patterns in a large online communication space. IADIS International Journal on WWW/Internet 6(1), 61–76 (2007)Google Scholar
  11. 11.
    Lampe, C., Resnick, P.: Slash(dot) and burn: distributed moderation in a large online conversation space. In: Conference on Human Factors in Computing Systems (2004)Google Scholar
  12. 12.
    Lauterbach, D., Truong, H., Shah, T., Adamic, L.: Surfing a web of trust: reputation and reciprocity on couchsurfing.com. In: 4th International Conference on Computer Science and Education (2009)Google Scholar
  13. 13.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Prediction positive and negative links in online social networks. In: 19th International World Wide Web Conference (2010)Google Scholar
  14. 14.
    Lin, M.J.J., Hung, S.W., Chen, C.J.: Fostering the determinants of knowledge sharing in professional virtual communities. Computers in Human Behavior 25(4), 929–939 (2009)CrossRefGoogle Scholar
  15. 15.
    Massa, P.: A survey of trust use and modeling in real online systems. In: Trust in E-services: Technologies, Practices and Challenges (2007)Google Scholar
  16. 16.
    Massa, P., Avesani, P.: Controversial users demand local trust metrics: an experimental study on epinions.com community. In: 20th National Conference on Artificial Intelligence (2005)Google Scholar
  17. 17.
    Mui, L., Mohtashemi, M., Halberstadt, A.: A computational model of trust and reputation. In: 35th Hawaii International Conference on System Sciences (2002)Google Scholar
  18. 18.
    O’Donovan, J., Smyth, B.: Trust in recommender systems. In: 10th International Conference on Intelligent User Interfaces (2005)Google Scholar
  19. 19.
    Skopik, F., Truong, H.-L., Dustdar, S.: Trust and Reputation Mining in Professional Virtual Communities. In: Gaedke, M., Grossniklaus, M., Díaz, O. (eds.) ICWE 2009. LNCS, vol. 5648, pp. 76–90. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Terveen, L., Hill, W.: Beyond recommender systems: helping people help each other. In: Human Computer Interaction in the New Millennium (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lizi Zhang
    • 1
  • Cheun Pin Tan
    • 1
  • Siyi Li
    • 1
  • Hui Fang
    • 1
  • Pramodh Rai
    • 1
  • Yao Chen
    • 1
  • Rohit Luthra
    • 1
  • Wee Keong Ng
    • 1
  • Jie Zhang
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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