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)


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.


Root Mean Square Error Random Walk Recommender System Neighborhood Size Cosine Similarity 


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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

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