Overlay Management for Fully Distributed User-Based Collaborative Filtering

  • Róbert Ormándi
  • István Hegedűs
  • Márk Jelasity
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6271)

Abstract

Offering personalized recommendation as a service in fully distributed applications such as file-sharing, distributed search, social networking, P2P television, etc, is an increasingly important problem. In such networked environments recommender algorithms should meet the same performance and reliability requirements as in centralized services. To achieve this is a challenge because a large amount of distributed data needs to be managed, and at the same time additional constraints need to be taken into account such as balancing resource usage over the network. In this paper we focus on a common component of many fully distributed recommender systems, namely the overlay network. We point out that the overlay topologies that are typically defined by node similarity have highly unbalanced degree distributions in a wide range of available benchmark datasets: a fact that has important—but so far largely overlooked—consequences on the load balancing of overlay protocols. We propose algorithms with a favorable convergence speed and prediction accuracy that also take load balancing into account. We perform extensive simulation experiments with the proposed algorithms, and compare them with known algorithms from related work on well-known benchmark datasets.

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References

  1. 1.
    Garbacki, P., Iosup, A., Doumen, J., Roozenburg, J., Yuan, Y., Brinke, T.M., Musat, L., Zindel, F., van der Werf, F., Meulpolder, M., et al.: Tribler protocol specificationGoogle Scholar
  2. 2.
    Kermarrec, A.M.: Challenges in personalizing and decentralizing the web: An overview of GOSSPLE. In: Guerraoui, R., Petit, F. (eds.) SSS 2009. LNCS, vol. 5873, pp. 1–16. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Adomavicius, G., Tuzhilin, E.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans. on Knowledge and Data Engineering 17, 734–749 (2005)CrossRefGoogle Scholar
  4. 4.
    Pitsilis, G., Marshall, L.: A trust-enabled P2P recommender system. In: Proc. 15th IEEE Intl. Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE 2006), pp. 59–64 (2006)Google Scholar
  5. 5.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proc. 22nd annual Intl. ACM SIGIR Conf. on Research and development in information retrieval (SIGIR 1999), pp. 230–237. ACM, New York (1999)CrossRefGoogle Scholar
  6. 6.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proc. 1994 ACM Conf. on Computer supported cooperative work (CSCW 1994), pp. 175–186. ACM, New York (1994)CrossRefGoogle Scholar
  7. 7.
    Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: Proc. 15th Intl. Conf. on Machine Learning (ICML 1998), pp. 46–54. Morgan Kaufmann, San Francisco (1998)Google Scholar
  8. 8.
    Park, Y.-J., Tuzhilin, A.: The long tail of recommender systems and how to leverage it. In: Proc. 2008 ACM Conf. on Recommender systems (RecSys 2008), pp. 11–18. ACM, New York (2008)CrossRefGoogle Scholar
  9. 9.
    Takács, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. Journal of Machine Learning Research 10, 623–656 (2009)Google Scholar
  10. 10.
    Lawrence, N.D., Urtasun, R.: Non-linear matrix factorization with gaussian processes. In: Proc. 26th Annual Intl. Conf. on Machine Learning (ICML 2009), pp. 601–608. ACM, New York (2009)Google Scholar
  11. 11.
    O‘Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Workshop on Recommender Systems at 22nd ACM SIGIR (1999)Google Scholar
  12. 12.
    Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)CrossRefMATHGoogle Scholar
  13. 13.
    Castagnos, S., Boyer, A.: Modeling preferences in a distributed recommender system. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 400–404. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Tveit, A.: Peer-to-peer based recommendations for mobile commerce. In: Proc. 1st Intl. workshop on Mobile commerce (WMC 2001), pp. 26–29. ACM, New York (2001)CrossRefGoogle Scholar
  15. 15.
    Bakker, A., Ogston, E., van Steen, M.: Collaborative filtering using random neighbours in peer-to-peer networks. In: Proc. 1st ACM Intl. workshop on Complex networks meet information & knowledge management (CNIKM 2009), pp. 67–75. ACM, New York (2009)CrossRefGoogle Scholar
  16. 16.
    Bickson, D., Malkhi, D., Zhou, L.: Peer-to-Peer rating. In: Proc. 7th IEEE Intl. Conf. on Peer-to-Peer Computing, 2007 (P2P 2007), pp. 211–218. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  17. 17.
    Han, P., Xie, B., Yang, F., Shen, R.: A scalable P2P recommender system based on distributed collaborative filtering. Expert Systems with Applications 27(2), 203–210 (2004)CrossRefGoogle Scholar
  18. 18.
    Wang, J., de Vries, A.P., Reinders, M.J.T.: Unified relevance models for rating prediction in collaborative filtering. ACM Trans. on Information Systems (TOIS) 26(3), 1–42 (2008)CrossRefGoogle Scholar
  19. 19.
    Pouwelse, J., Yang, J., Meulpolder, M., Epema, D., Sips, H.: Buddycast: an operational peer-to-peer epidemic protocol stack. In: Proc. 14th Annual Conf. of the Advanced School for Computing and Imaging, ASCI, pp. 200–205 (2008)Google Scholar
  20. 20.
    Voulgaris, S., van Steen, M.: Epidemic-style management of semantic overlays for content-based searching. In: Cunha, J.C., Medeiros, P.D. (eds.) Euro-Par 2005. LNCS, vol. 3648, pp. 1143–1152. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  21. 21.
    Garbacki, P., Epema, D.H.J., van Steen, M.: A two-level semantic caching scheme for super-peer networks. In: Proc. 10th Intl. Workshop on Web Content Caching and Distribution (WCW 2005), pp. 47–55. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar
  22. 22.
    Akavipat, R., Wu, L.S., Menczer, F., Maguitman, A.: Emerging semantic communities in peer web search. In: Proc. Intl. workshop on Information retrieval in peer-to-peer networks (P2PIR 2006), pp. 1–8. ACM, New York (2006)Google Scholar
  23. 23.
    Ziegler, C.N., McNee, S.M., Konstan, J.A., Lausen, G.: Improving recommendation lists through topic diversification. In: Proc. 14th Intl. Conf. on WWW, pp. 22–32. ACM, New York (2005)Google Scholar
  24. 24.
    Jelasity, M., Voulgaris, S., Guerraoui, R., Kermarrec, A.M., van Steen, M.: Gossip-based peer sampling. ACM Trans. on Computer Systems 25(3), 8 (2007)CrossRefGoogle Scholar
  25. 25.
    Jelasity, M., Montresor, A., Babaoglu, O.: T-Man: Gossip-based fast overlay topology construction. Computer Networks 53(13), 2321–2339 (2009)CrossRefMATHGoogle Scholar
  26. 26.
    Montresor, A., Jelasity, M.: Peersim: A scalable P2P simulator. In: Proc. Ninth IEEE Intl. Conf. on Peer-to-Peer Computing (P2P 2009), pp. 99–100. IEEE, Los Alamitos (2009) (extended abstract)CrossRefGoogle Scholar
  27. 27.
    Jelasity, M., Montresor, A., Jesi, G.P., Voulgaris, S.: The Peersim simulator, http://peersim.sf.net

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Róbert Ormándi
    • 1
  • István Hegedűs
    • 1
  • Márk Jelasity
    • 2
  1. 1.University of SzegedHungary
  2. 2.University of Szeged and Hungarian Academy of SciencesHungary

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