Abstract
Decentralised recommenders have been proposed to deliver privacy-preserving, personalised and highly scalable on-line recommendations. Current implementations tend, however, to rely on a hard-wired similarity metric that cannot adapt. This constitutes a strong limitation in the face of evolving needs. In this paper, we propose a framework to develop dynamically adaptive decentralised recommendation systems. Our proposal supports a decentralised form of adaptation, in which individual nodes can independently select, and update their own recommendation algorithm, while still collectively contributing to the overall system’s mission.
Keywords
Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Movielens 1 million ratings dataset, http://grouplens.org/datasets/movielens
Tribler, http://www.tribler.org
Bai, X., Bertier, M., Guerraoui, R., Kermarrec, A.-M., Leroy, V.: Gossiping personalized queries. In: EDBT 2010 (2010)
Baraglia, R., Dazzi, P., Mordacchini, M., Ricci, L.: A peer-to-peer recommender system for self-emerging user communities based on gossip overlays. J. of Comp. and Sys. Sciences (2013)
Bertier, M., Frey, D., Guerraoui, R., Kermarrec, A.-M., Leroy, V.: The gossple anonymous social network. In: Gupta, I., Mascolo, C. (eds.) Middleware 2010. LNCS, vol. 6452, pp. 191–211. Springer, Heidelberg (2010)
Boutet, A., Frey, D., Guerraoui, R., Jégou, A., Kermarrec, A.-M.: Privacy-Preserving Distributed Collaborative Filtering. In: Noubir, G., Raynal, M. (eds.) NETYS 2014. LNCS, vol. 8593, pp. 169–184. Springer, Heidelberg (2014)
Boutet, A., Frey, D., Guerraoui, R., Jégou, A., Kermarrec, A.-M.: WhatsUp Decentralized Instant News Recommender. In: IPDPS (2013)
Carretero, J., Isaila, F., Kermarrec, A.-M., Taïani, F., Tirado, J.M.: Geology: Modular georecommendation in gossip-based social networks. In: ICDCS 2012 (2012)
Das, A.S., Datar, M., Garg, A., Rajaram, S.: Google news personalization: scalable online collaborative filtering. In: WWW (2007)
Demers, A., Greene, D., Hauser, C., Irish, W., Larson, J., Shenker, S., Sturgis, H., Swinehart, D., Terry, D.: Epidemic Algorithms for Replicated Database Maintenance. In: PODC 1987 (1987)
Facebook Inc. Facebook: Company info – statistics (March 2014), https://newsroom.fb.com/company-info/ (accessed: May 13, 2014)
Frey, D., Kermarrec, A.-M., Maddock, C., Mauthe, A., Taïani, F.: Adaptation for the masses: Towards decentralized adaptation in large-scale p2p recommenders. In: 13th Workshop on Adaptive & Reflective Middleware, ARM 2014 (2014)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. In: CACM (1992)
Han, P., Xie, B., Yang, F., Shen, R.: A scalable p2p recommender system based on distributed collaborative filtering. Expert Systems with Applications (2004)
Hegedus, I., Ormándi, R., Jelasity, M.: Gossip-based learning under drifting concepts in fully distributed networks. In: SASO 2012 (2012)
Jelasity, M., Montresor, A., Babaoglu, O.: T-man: Gossip-based fast overlay topology construction. Computer Networks 53(13), 2321–2339 (2009)
Jelasity, M., Voulgaris, S., Guerraoui, R., Kermarrec, A.-M., van Steen, M.: Gossip-based peer sampling. In: ACM TOCS, vol. 25 (2007)
Kermarrec, A.-M., Leroy, V., Moin, A., Thraves, C.: Application of random walks to decentralized recommender systems. In: Lu, C., Masuzawa, T., Mosbah, M. (eds.) OPODIS 2010. LNCS, vol. 6490, pp. 48–63. Springer, Heidelberg (2010)
Kermarrec, A.-M., Taïani, F.: Diverging towards the common good: heterogeneous self-organisation in decentralised recommenders. In: SNS 2012 (2012)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: Applying collaborative filtering to usenet news. In: CACM (1997)
Leroy, V., Cambazoglu, B.B., Bonchi, F.: Cold start link prediction. In: KDD 2010 (2010)
Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Computing (2003)
Mega, G., Montresor, A., Picco, G.P.: Efficient dissemination in decentralized social networks. In: IEEE P2P 2011 (2011)
Miller, B.N., Konstan, J.A., Riedl, J.: Pocketlens: Toward a personal recommender system. In: TOIS (2004)
Moreno, A., Castro, H., Riveill, M.: Decentralized recommender systems for mobile advertisement. In: Workshop on Personalization in Mobile Applications (PEMA 2011). ACM, New York (2011)
Olsson, T.: Decentralised social filtering based on trust. In: AAAI 1998 Recommender Systems Workshop (1998)
Schiavoni, V., Rivière, E., Felber, P.: Whisper: Middleware for confidential communication in large-scale networks. In: ICDCS 2011 (June 2011)
Song, Y., Dixon, S., Pearce, M.: A survey of music recommendation systems and future perspectives. In: CMMR 2012 (2012)
Tirado, J.M., Higuero, D., Isaila, F., Carretero, J., Iamnitchi, A.: Affinity p2p: A self-organizing content-based locality-aware collaborative peer-to-peer network. Comp. Net. 54 (2010)
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)
Yeung, C.-M.A., Liccardi, I., Lu, K., Seneviratne, O., Berners-Lee, T.: Decentralization: The future of online social networking. In: W3C Workshop on the Future of Social Networking (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 IFIP International Federation for Information Processing
About this paper
Cite this paper
Frey, D., Kermarrec, AM., Maddock, C., Mauthe, A., Roman, PL., Taïani, F. (2015). Similitude: Decentralised Adaptation in Large-Scale P2P Recommenders. In: Bessani, A., Bouchenak, S. (eds) Distributed Applications and Interoperable Systems. DAIS 2015. Lecture Notes in Computer Science(), vol 9038. Springer, Cham. https://doi.org/10.1007/978-3-319-19129-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-19129-4_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19128-7
Online ISBN: 978-3-319-19129-4
eBook Packages: Computer ScienceComputer Science (R0)