Application of Random Walks to Decentralized Recommender Systems

  • Anne-Marie Kermarrec
  • Vincent Leroy
  • Afshin Moin
  • Christopher Thraves
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6490)

Abstract

The need for efficient decentralized recommender systems has been appreciated for some time, both for the intrinsic advantages of decentralization and the necessity of integrating recommender systems into P2P applications. On the other hand, the accuracy of recommender systems is often hurt by data sparsity. In this paper, we compare different decentralized user-based and item-based Collaborative Filtering (CF) algorithms with each other, and propose a new user-based random walk approach customized for decentralized systems, specifically designed to handle sparse data. We show how the application of random walks to decentralized environments is different from the centralized version. We examine the performance of our random walk approach in different settings by varying the sparsity, the similarity measure and the neighborhood size. In addition, we introduce the popularizing disadvantage of the significance weighting term traditionally used to increase the precision of similarity measures, and elaborate how it can affect the performance of the random walk algorithm. The simulations on MovieLens 10,000,000 ratings dataset demonstrate that over a wide range of sparsity, our algorithm outperforms other decentralized CF schemes. Moreover, our results show decentralized user-based approaches perform better than their item-based counterparts in P2P recommender applications.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
  2. 2.
    Tribler (2010), http://www.tribler.org
  3. 3.
    Azar, Y., Fiat, A., Karlin, A.R., Mcsherry, F., Saia, J.: Spectral analysis of data. In: ACM Symposium on Theory of Computing, pp. 619–626 (2001)Google Scholar
  4. 4.
    Biau, G., Cadre, B., Rouviere, L.: A stochastic model for collaborative recommendation. The Annals of Statistics (2009)Google Scholar
  5. 5.
    Canny, J., Sorkin, S.: Practical large-scale distributed key generation. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 138–152. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Deshpande, M., Karypis, G.: Item-based top-n recommendation algorithms. ACM Trans. Inf. Syst. 22(1), 143–177 (2004)CrossRefGoogle Scholar
  7. 7.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: ACM SIGIR, pp. 230–237 (1999)Google Scholar
  8. 8.
    Jamali, M., Ester, M.: Trustwalker: a random walk model for combining trust-based and item-based recommendation. In: ACM SIGKDD, pp. 397–406 (2009)Google Scholar
  9. 9.
    Jelasity, M., Montresor, A., Babaoglu, O.: T-man: Gossip-based fast overlay topology construction. IJCNC (2009)Google Scholar
  10. 10.
    Jelasity, M., Voulgaris, S., Guerraoui, R., Kermarrec, A.-M., van Steen, M.: Gossip-based peer sampling. ACM Trans. Comput. Syst. 25(3), 8 (2007)CrossRefGoogle Scholar
  11. 11.
    Pazzani, M.J.: A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 393–408 (1999)Google Scholar
  12. 12.
    Kermarrec, A.-M., Leroy, V., Moin, A., Thraves, C.: Addressing sparsity in decentralized recommender systems through random walks. Technical report, INRIA (2010)Google Scholar
  13. 13.
    Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proc. of the 14th ACM SIGKDD, pp. 426–434 (2008)Google Scholar
  14. 14.
    Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Recommendation systems: A probabilistic analysis. In: Proc. IEEE Symp. on Foundations of Computer Science (1998)Google Scholar
  15. 15.
    Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing, 76–80 (2003)Google Scholar
  16. 16.
    Miller, B.N., Konstan, J.A., Riedl, J.: Pocketlens: Toward a personal recommender system. ACM Trans. Inf. Syst. 22(3), 437–476 (2004)CrossRefGoogle Scholar
  17. 17.
    Breeze, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Uncertainty in Artificial Intelligence (1998)Google Scholar
  18. 18.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: World Wide Web, pp. 285–295 (2001)Google Scholar
  19. 19.
    Tsoumakos, D., Roussopoulos, N.: Adaptive probabilistic search for peer-to-peer networks. In: P2P, pp. 102–109 (2003)Google Scholar
  20. 20.
    Voulgaris, S., Steen, M.V.: 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.
    Yildirim, H., Krishnamoorthy, M.S.: A random walk method for alleviating the sparsity problem in collaborative filtering. In: Proc. of the ACM Conf. on Recommender Systems, pp. 131–138 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Anne-Marie Kermarrec
    • 1
  • Vincent Leroy
    • 2
  • Afshin Moin
    • 1
  • Christopher Thraves
    • 1
  1. 1.INRIA Rennes - Bretagne AtlantiqueRennesFrance
  2. 2.INSA de Rennes, UEBRennesFrance

Personalised recommendations