Abstract
In this paper, we propose a novel recommender framework for partially decentralized file sharing Peer-to-Peer systems. The proposed recommender system is based on user-based collaborative filtering. We take advantage from the partial search process used in partially decentralized systems to explore the relationships between peers. The proposed recommender system does not require any additional effort from the users since implicit rating is used. The recommender system also does not suffer from the problems that traditional collaborative filtering schemes suffer from like the Cold start and the Data sparseness. To measure the similarity between peers, we propose Files’ Popularity Based Recommendation (FP) and Asymmetric Peers’ Similarity Based Recommendation with File Popularity (ASFP). We also investigate similarity metrics that were proposed in other fields and adapt them to file sharing P2P systems. We analyze the impact of each similarity metric on the accuracy of the recommendations. Both weighted and non weighted approaches were studied.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adar E, Huberman B (2000) Free riding on gnutella. Tech. rep., HP
Anderberg M (1973) Cluster analysis for applications. Academic Press
Deshpande M, Karypis G (2004) Item-based top-N recommendation algorithms. ACM Trans Inf Sys 22(1):143–177
Dice L (1945) Measures of the amount of ecological association between species. Ecology 26:297–302
Duarte J, dos Santos J, Melo L (1999) Comparison of similarity coefficients based on RAPD markers in the common bean. Genet Mol Biol 22(3):427–432
Gummadi K, Dunn RJ, Saroiu S, Gribble SD, Levy HM, Zahorjan J (2003) Measurement, modeling, and analysis of a Peer-to-Peer file sharing workload. In: ACM symposium on operating systems principles, pp 314–329
Jaccard P (1901) Etude Comparative de la Distribuition Florale dans une Portion des Alpes et de Jura. Bull Soc Vaud Sci Nat 37:547–579
Jamali M, Ester M (2009) Using a trust network to improve top-N recommendation. In: ACM conference on recommender systems, pp 181–188
Karypis G (2001) Evaluation of item-based top-N recommendation algorithms. In: International conference on information and knowledge management, pp 247–254
Kulczynski S (1927) Classe des sciences mathématiques et naturelles. l’Acadamie Polonaise des Sciences et des Lettres II:57–203
Lesot M, Rifqi M, Benhadda H (2009) Similarity measures for binary numerical data: a survey. IJKESDP 1(1):63–84
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. In: IEEE internet computing, pp 76–80
Lourenço F, Lobo V, Baçao F (2004) Binary-based similarity measures for categorical data and their application in self-organizing maps. In: JOCLAD
Massa P, Avesani P (2004) Trust-aware collaborative filtering for recommender systems. In: International conference on cooperative information systems
Mekouar L, Iraqi Y, Boutaba R (2009) A contribution-based service differentiation scheme for Peer-to-Peer systems. Int J Peer-to-Peer Netw Appl 2(2):146–163
Ochiai A (1957) Zoogeographic studies on the soleoid fishes. Japanese Society for Fish Science 22:526–530
PeerSim (2003). http://peersim.sourceforge.net/
Rogers J, Tanimoto T (1960) A computer program for classifying plants. Science 132:1115–1118
Ruffo G, Schifanella R, Ghiringhello E (2006) A decentralized recommendation system based on self-organizing partnerships. In: IFIP networking, pp 618–629
Russel P, Rao T (1940) On habitat and association of species of anopheline larvae in South-Eastern Madras, vol 3, pp 153–178
Sarwar B, Karypis G, Konstan J, Reidl J (2000) Analysis of recommendation algorithms for e-commerce. In: EC, pp 158–167
Sarwar B, Karypis G, Konstan J, Reidl J (2001) Item-based collaborative filtering recommendation algorithms. In: International conference on world wide web, pp 285–295
Sokal R, Michener C (1958) A statistical method for evaluating systematic relationships. Society of University of Kansas 38:1409–1438
Sokal R, Sneath P (1963) Principles of numeric taxonomy. W.H. Freeman
Wang J, Pouwelse J, Lagendijk RL, Reinders MJT (2006) Distributive collaborative filtering for Peer-to-Peer file sharing systems. In: ACM symposium on applied computing, pp 1026–1030
Wang J, Pouwelse JA, Fokker J, de Vries A, Reinders M (2008) Personalization on a Peer-to-Peer television system. Multimedia Tools Appl 36:89–113
Acknowledgements
This work was supported in part by the Natural Science and Engineering Council of Canada (NSERC) under its Discovery program, and the WCU (World Class University) program through the Korea Science and Engineering Foundation funded by the Ministry of Education, Science and Technology (Project No. R31-2008-000-10100-0).
Author information
Authors and Affiliations
Corresponding author
Additional information
Preliminary versions were accepted in the IEEE International Workshop on Enabling Technologies: Infrastructures for Collaborative Enterprises, Collaborative Peer-to-Peer Systems Workshop (COPS), 2008 and in the tenth IEEE International Conference on Computer and Information Technology (CIT), 2010.
Rights and permissions
About this article
Cite this article
Mekouar, L., Iraqi, Y. & Boutaba, R. An analysis of peer similarity for recommendations in P2P systems. Multimed Tools Appl 60, 277–303 (2012). https://doi.org/10.1007/s11042-010-0612-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-010-0612-1