Advertisement

World Wide Web

, Volume 18, Issue 1, pp 111–137 | Cite as

A personalized credibility model for recommending messages in social participatory media environments

  • Aaditeshwar Seth
  • Jie Zhang
  • Robin Cohen
Article

Abstract

We propose a method to determine the credibility of messages that are posted in participatory media (such as blogs), of use in recommender systems designed to provide users with messages that are considered to be the most credible to them. Our approach draws from theories developed in sociology, political science, and information science—we show that the social context of users influences their opinion about the credibility of messages they read, and that this context can be captured by analyzing the social network of users. We use this insight to improve recommendation algorithms for messages created in participatory media environments. Our methodology rests on Bayesian learning, integrating new concepts of context and completeness of messages inspired by the strength of weak ties hypothesis from social network theory. We show that our credibility evaluation model can be used to significantly enhance the performance of collaborative filtering recommendation. Experimental validation is done using datasets obtained from social networking websites used for knowledge sharing. We conclude by clarifying our relationship to the semantic adaptive social web, emphasizing our use of personal evaluations of messages and the social network of users, instead of merely automated semantic interpretation of content.

Keywords

Social networks Recommender systems Credibility modeling Participatory media 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Adamic, L., Adar, E.: How to search a social network. Soc. Networks 27(3), 187–203 (2005)CrossRefGoogle Scholar
  2. 2.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  3. 3.
    Baybeck, B., Huckfeldt, R.: Urban contexts, spatially dispersed networks, and the diffusion of political information. Political Geography 21(2), 195–220 (2002)CrossRefGoogle Scholar
  4. 4.
    Brownstein, C.A., Brownstein, J.S. III, D.S.W., Wicks, P., Heywood, J.A.: The power of social networking in medicine. Nat. Biotechnol. 27, 888–890 (2009)CrossRefGoogle Scholar
  5. 5.
    Bryant, J., Zillmann, D.: Media Effects: Advances in Theory and Research. Lawrence Erlbaum Associates (2002)Google Scholar
  6. 6.
    Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233–240 (2006)Google Scholar
  7. 7.
    Fang, H., Zhang, J., Thalmann, N.: A trust model stemmed from the diffusion theory for opinion evaluation. In: Proceedings of the 12th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2013)Google Scholar
  8. 8.
    Fogg, B.J., Tseng, H.: The elements of computer credibility. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 80–87 (1999)Google Scholar
  9. 9.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Gillmor, D.: We the Media: Grassroots Journalism by the People, for the People. O’Reilly Media (2006)Google Scholar
  11. 11.
    Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78(6), 1360–1380 (1973)CrossRefGoogle Scholar
  12. 12.
    Guha, R., Kumar, R., Raghavan, P., Tomkins, A.: Propagation of trust and distrust. In: Proceedings of the 13th International Conference on World Wide Web (WWW), pp. 403–412 (2004)Google Scholar
  13. 13.
    Guo, G., Zhang, J., Thalmann, D.: A simple but effective method to incorporate trusted neighbors in recommender systems. In: Proceedings of the 20th International Conference on User Modeling, Adaptation and Personalization (UMAP) (2012)Google Scholar
  14. 14.
    Hang, C.W., Wang, Y., Singh, M.P.: Operators for propagating trust and their evaluation in social networks. In: Proceedings of the 8th International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), pp. 1025–1032 (2009)Google Scholar
  15. 15.
    Hang, C.W., Zhang, Z., Singh, M.: Generalized trust propagation with limited evidence. Computer (2012). doi:10.1109/MC.2012.116 Google Scholar
  16. 16.
    Huynh, T.D., Jennings, N.R., Shadbolt, N.R.: FIRE: an integrated trust and reputation model for open multi-agent systems. In: Proceedings of the 16th European Conference on Artificial Intelligence (ECAI), pp. 18–22 (2004)Google Scholar
  17. 17.
    Kale, A., Karandikar, A., Kolari, P., Java, A., Finin, T., Joshi, A.: Modeling trust and influence in the blogosphere using link polarity. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM) (2007)Google Scholar
  18. 18.
    Kamvar, S.D., Haveliwala, T.H., Manning, C.D., Golub, G.H.: Exploiting the block structure of the web for computing pagerank. Tech. rep., Stanford University (2003)Google Scholar
  19. 19.
    Kamvar, S.D., Schlosser, M.T., Garcia-Molina, H.: The eigentrust algorithm for reputation management in P2P networks. In: Proceedings of the 12th International Conference on World Wide Web (WWW) (2003)Google Scholar
  20. 20.
    Kleinberg, J.: Authoritative sources in a hyperlinked environment. In: Proceedings of ACM-SIAM Symposium on Discrete Algorithms (1998)Google Scholar
  21. 21.
    Kolari, P., Finin, T., Lyons, K., Yesha, Y., Yesha, Y., Perelgut, S., Hawkins, J.: On the structure, properties, and utility of internal corporate blogs. In: Proceedings of the International Conference on Weblogs and Social Media (ICWSM) (2007)Google Scholar
  22. 22.
    Kuter, U., Golbeck, J.: SUNNY: a new algorithm for trust inference in social networks using probabilistic confidence models. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence (2007)Google Scholar
  23. 23.
    Lerman, K.: Social information processing in news aggregation. IEEE Internet Computing 11(6), 16–28 (2007)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Minhas, U.F., Zhang, J., Tran, T., Cohen, R.: A multi-faceted approach to modeling agent trust for effective communication in the application of mobile ad hoc vehicular networks. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. (SMCC) 41(3), 407–420 (2011)CrossRefGoogle Scholar
  25. 25.
    Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement (IMC) (2007)Google Scholar
  26. 26.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Tech. rep., Stanford University (1999)Google Scholar
  27. 27.
    Pujol, J.M., Sanguesa, R., Delgado, J.: Extracting reputation in multi agent systems by means of social network topology. In: Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2002)Google Scholar
  28. 28.
    Rieh, S.Y.: Judgement of information quality and cognitive authority on the web. J. Am. Soc. Inf. Sci. Technol. 53(2), 145–161 (2002)CrossRefGoogle Scholar
  29. 29.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall (2009)Google Scholar
  30. 30.
    Sabater, J., Sierra, C.: REGRET: a reputation model for gregarious societies. In: Proceedings of the 5th International Joint Conference on Autonomous Agents (AAMAS) Workshop on Deception, Fraud and Trust in Agent Societies (TRUST), pp. 61–69 (2001)Google Scholar
  31. 31.
    Sakuma, P.: The Future of Facebook. Time. Retrieved on 5 Mar 2008 (2007)Google Scholar
  32. 32.
    Seth, A.: Design of a recommender system for participatory media. Ph.D. thesis, University of Waterloo (2008)Google Scholar
  33. 33.
    Seth, A., Zhang, J.: A social network based approach to personalized recommendation of participatory media content. In: Proceedings of International AAAI Conference on Weblogs and Social Media (2008)Google Scholar
  34. 34.
    Sifry, D.: The State of the Live Web. http://www.sifry.com/alerts/archives/000493.html (2007). Accessed 5 April 2007
  35. 35.
    Song, X., Tseng, B.L., Lin, C.Y., Sun, M.T.: Personalized recommendation driven by information flow. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2006)Google Scholar
  36. 36.
    Tantipathananandh, C., Berger-Wolf, T., Kempe, D.: A framework for community identification in dynamic social networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD) (2007)Google Scholar
  37. 37.
    van Dongen, S.: Mcl: a cluster algorithm for graphs. Ph.D. thesis, University of Utrecht (2000)Google Scholar
  38. 38.
    Walsh, K., Sirer, E.G.: Experience with an object reputation system for Peer-to-Peer filesharing. In: Proceedings of the 3rd Conference on Networked Systems Design and Implementation (NSDI) (2006)Google Scholar
  39. 39.
    Wasserman, L.: All of Statistics: A Concise Course in Statistical Inference. Springer, New York (2004)CrossRefGoogle Scholar
  40. 40.
    Whitby, A., Jøsang, A., Indulska, J.: Filtering out unfair ratings in bayesian reputation systems. In: Proceedings of the 3rd International Joint Conference on Autonomous Agenst Systems (AAMAS) Workshop on Trust in Agent Societies (TRUST) (2004)Google Scholar
  41. 41.
    Yang, J., Wang, J., Clements, M., Pouwelse, J., de Vries, A.P., Reinders, M.: An epidemic-based P2P recommender system. In: Proceedings of the ACM SIGIR Workshop on Large Scale Distributed Systems for Information Retrieval (LSDS-IR) (2007)Google Scholar
  42. 42.
    Yu, B., Singh, M.P.: Searching social networks. In: Proceedings of the 2nd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2003)Google Scholar
  43. 43.
    Zhang, J., Cohen, R.: A comprehensive approach for sharing semantic web trust ratings. Comput. Intell. 23(3), 302–319 (2007)CrossRefMathSciNetGoogle Scholar
  44. 44.
    Zhang, L., Jiang, S., Zhang, J., Ng, W.K.: Robustness of trust models and combinations for handling unfair ratings. In: Proceedings of the 6th IFIP WG 11.11 International Conference on Trust Management (IFIPTM) (2012)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology DelhiNew DelhiIndia
  2. 2.School of Computer EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.School of Computer ScienceUniversity of WaterlooWaterlooCanada

Personalised recommendations