Autonomous Agents and Multi-Agent Systems

, Volume 25, Issue 3, pp 475–498 | Cite as

Generalized framework for personalized recommendations in agent networks

Article

Abstract

An agent network can be modeled as a directed weighted graph whose vertices represent agents and edges represent a trust relationship between the agents. This article proposes a new recommendation approach, dubbed LocPat, which can recommend trustworthy agents to a requester in an agent network. We relate the recommendation problem to the graph similarity problem, and define the similarity measurement as a mutually reinforcing relation. We understand an agent as querying an agent network to which it belongs to generate personalized recommendations. We formulate a query into an agent network as a structure graph applied in a personalized manner that reflects the pattern of relationships centered on the requesting agent. We use this pattern as a basis for recommending an agent or object (a vertex in the graph). By calculating the vertex similarity between the agent network and a structure graph, we can produce a recommendation based on similarity scores that reflect both the link structure and the trust values on the edges. Our resulting approach is generic in that it can capture existing network-based approaches merely through the introduction of appropriate structure graphs. We evaluate different structure graphs with respect to two main kinds of settings, namely, social networks and ratings networks. Our experimental results show that our approach provides personalized and flexible recommendations effectively and efficiently based on local information.

Keywords

Agent mining Personalized recommendation Social networks Ratings networks Trust 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Artz D., Gil Y. (2007) A survey of trust in computer science and the semantic web. Journal of Web Semantics 5(2): 58–71CrossRefGoogle Scholar
  2. 2.
    Ben-Shimon, D., Tsikinovsky, A., Rokach, L., Meisels, A., Shani, G., Naamani, L. (2007). Recommender system from personal social networks. In: Proceedings of the 5th Atlantic Web Intelligence Conference (pp. 47–55). Paris: Springer Berlin/Heidelberg.Google Scholar
  3. 3.
    Blondel V. D., Gajardo A., Heymans M., Senellart P., Dooren P. V. (2004) A measure of similarity between graph vertices: Applications to synonym extraction and web searching. SIAM Review 46(4): 647–666MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Brin S., Page L. (1998) The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30(1–7): 107–117CrossRefGoogle Scholar
  5. 5.
    Cao L., Gorodetsky V., Mitkas P. A. (2009) Agent mining: The synergy of agents and data mining. IEEE Intelligent Systems 24(3): 64–72CrossRefGoogle Scholar
  6. 6.
    Fouss F., Pirotte A., Renders J. M., Saerens M. (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering 19(3): 355–369CrossRefGoogle Scholar
  7. 7.
    Giannella C., Bhargava R., Kargupta H. (2004) Multi-agent systems and distributed data mining. In: Klusch M., Ossowski S., Kashyap V., Unland R. (eds) Cooperative information agents VIII, Lecture Notes in Computer Science, vol. 3191.. Springer, Berlin/Heidelberg, pp 1–15CrossRefGoogle Scholar
  8. 8.
    Golub G. H., Loan C. F. V. (1996) Matrix computations (3rd ed.). The Johns Hopkins University Press, BaltimoreMATHGoogle Scholar
  9. 9.
    Gray, E., Seigneur, J. M., Chen, Y., Jensen, C. (2003). Trust propagation in small worlds. In: Proceedings of the 1st International Conference on Trust Management (pp. 239–254). Berlin, Heidelberg: Springer-Verlag.Google Scholar
  10. 10.
    Guha, R., Kumar, R., Raghavan, P., Tomkins, A. (2004). Propagation of trust and distrust. In: WWW: Proceedings of the 13th International Conference on World Wide Web (pp. 403–412). New York: ACM Press.Google Scholar
  11. 11.
    Hang C. W., Singh M. P. (2011) Trustworthy service selection and composition. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 6(1): 5:1–5:17Google Scholar
  12. 12.
    Hang, C. W., Wang, Y., Singh, M. P. (2009). Operators for propagating trust and their evaluation in social networks. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (pp. 1025–1032, vol. 2). Budapest: IFAAMAS.Google Scholar
  13. 13.
    Jeh, G., Widom, J. (2002). SimRank: a measure of structural-context similarity. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 538–543). New York, NY: ACM Press.Google Scholar
  14. 14.
    Kamvar, S. D., Schlosser, M. T., Garcia-Molina, H. (2003). The EigenTrust algorithm for reputation management in P2P networks. In: WWW: Proceedings of the 12th International Conference on World Wide Web (pp. 640–651). New York: ACM Press.Google Scholar
  15. 15.
    Katz, Y., Golbeck, J. (2006). Social network-based trust in prioritized default logic. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI) (pp. 1345–1350). Menlo Park: AAAI Press.Google Scholar
  16. 16.
    Kleinberg J. M. (1999) Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5): 604–632MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Klusch, M., Lodi, S., Moro, G. (2003). The role of agents in distributed data mining: Issues and benefits. In: Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology (pp. 211–217). Washington, DC: IEEE Computer Society.Google Scholar
  18. 18.
    Kunegis, J., Lommatzsch, A. (2009). Learning spectral graph transformations for link prediction. In: Proceedings of the 26th Annual International Conference on Machine Learning (pp. 561–568). New York, NY: ACM Press.Google Scholar
  19. 19.
    Kuter U., Golbeck J. (2010) Using probabilistic confidence models for trust inference in web-based social networks. ACM Transactions on Internet Technology (TOIT) 10(2): 1–23CrossRefGoogle Scholar
  20. 20.
    Leicht E. A., Holme P., Newman M. E. J. (2006) Vertex similarity in networks. Physical Review E 73: 026,120CrossRefGoogle Scholar
  21. 21.
    Levien, R. (2003). Attack resistant trust metrics. PhD thesis, UC BerkeleyGoogle Scholar
  22. 22.
    Liben-Nowell D., Kleinberg J. (2007) The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58(7): 1019–1031CrossRefGoogle Scholar
  23. 23.
    Lorrain F., White H.C. (1971) Structural equivalence of individuals in social networks. Journal of Mathematical Sociology 1: 49–80CrossRefGoogle Scholar
  24. 24.
    Melnik, S., Garcia-Molina, H., Rahm, E. (2002). Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: Proceedings of the 18th International Conference on Data Engineering (pp. 117–128). Washington, DC: IEEE Computer Society.Google Scholar
  25. 25.
    Miller, B. N., Albert, I., Lam, S. K., Konstan, J. A., Riedl, J. (2003). MovieLens unplugged: Experiences with an occasionally connected recommender system. In: Proceedings of the 8th International Conference on Intelligent User Interfaces (IUI) (pp. 263–266). New York, NY: ACM Press.Google Scholar
  26. 26.
    Moemeng, C., Gorodetsky, V., Zuo, Z., Yang, Y., Zhang, C. (2009). Agent-based distributed data mining: A survey. In: Cao L. (Ed.) Data Mining and Multi-agent Integration (Chap. 3, pp. 47–58). New York: Springer.Google Scholar
  27. 27.
    Nathanson, T., Bitton, E., Goldberg, K. (2007). Eigentaste 5.0: Constant-time adaptability in a recommender system using item clustering. In: Proceedings of the ACM Conference on Recommender Systems (pp. 149–152). New York, NY: ACM Press.Google Scholar
  28. 28.
    Quercia, D., Hailes, S., Capra, L. (2007). Lightweight distributed trust propagation. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM) (pp. 282–291). Omaha.Google Scholar
  29. 29.
    Richardson, M., Agrawal, R., Domingos, P. (2003). Trust management for the semantic Web. In: The Semantic Web: Proceedings of the 2nd International Semantic Web Conference (ISWC), LNCS (vol. 2870, pp. 351–368). New York: Springer.Google Scholar
  30. 30.
    Shani, G., Chickering, M., Meek, C. (2008) Mining recommendations from the web. In: Proceedings of the ACM Conference on Recommender Systems (pp. 35–42). New York, NY: ACM Press.Google Scholar
  31. 31.
    Tavakolifard, M. (2010). Similarity-based techniques for trust management. In: Z. U. H. Usmani (ed.) Web Intelligence and Intelligent Agents (chap. 11, pp. 233–250). InTech.Google Scholar
  32. 32.
    Wang, Y., Singh, M. P. (2006). Trust representation and aggregation in a distributed agent system. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI) (pp. 1425–1430). Boston, MA: AAAI Press.Google Scholar
  33. 33.
    Yu B., Singh M. P. (2002) Distributed reputation management for electronic commerce. Computational Intelligence 18(4): 535–549MathSciNetCrossRefGoogle Scholar
  34. 34.
    Ziegler, C. N., Lausen, G. (2004). Spreading activation models for trust propagation. In: EEE: Proceedings of the IEEE International Conference on e-Technology, e-Commerce and e-Service (pp. 83–97). Washington, DC: IEEE Computer Society.Google Scholar

Copyright information

© The Author(s) 2011

Authors and Affiliations

  1. 1.North Carolina State UniversityRaleighUSA

Personalised recommendations