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.
M. Jelasity was supported by the Bolyai Scholarship of the Hungarian Academy of Sciences. This work was partially supported by the Future and Emerging Technologies programme FP7-COSI-ICT of the European Commission through project QLectives (grant no.: 231200).
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
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 specification
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)
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)
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)
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)
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)
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)
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)
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)
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)
O‘Connor, M., Herlocker, J.: Clustering items for collaborative filtering. In: Workshop on Recommender Systems at 22nd ACM SIGIR (1999)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Jelasity, M., Montresor, A., Babaoglu, O.: T-Man: Gossip-based fast overlay topology construction. Computer Networks 53(13), 2321–2339 (2009)
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)
Jelasity, M., Montresor, A., Jesi, G.P., Voulgaris, S.: The Peersim simulator, http://peersim.sf.net
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ormándi, R., Hegedűs, I., Jelasity, M. (2010). Overlay Management for Fully Distributed User-Based Collaborative Filtering. In: D’Ambra, P., Guarracino, M., Talia, D. (eds) Euro-Par 2010 - Parallel Processing. Euro-Par 2010. Lecture Notes in Computer Science, vol 6271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15277-1_43
Download citation
DOI: https://doi.org/10.1007/978-3-642-15277-1_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15276-4
Online ISBN: 978-3-642-15277-1
eBook Packages: Computer ScienceComputer Science (R0)