Multimedia Tools and Applications

, Volume 60, Issue 2, pp 277–303 | Cite as

An analysis of peer similarity for recommendations in P2P systems

  • Loubna MekouarEmail author
  • Youssef Iraqi
  • Raouf Boutaba


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.


Recommender system Similarity metrics Personalized recommendations Content adaptation Multimedia files Peer-to-Peer systems 



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).


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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.David R. Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada
  2. 2.Division of IT Convergence EngineeringPOSTECHPohangKorea
  3. 3.Department of Computer EngineeringKhalifa UniversitySharjahUAE

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