Application of Random Walks to Decentralized Recommender Systems

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 meas...

• Type: Journal
• Year: 2010
• Where: OPODIS
• Authors: Anne-Marie Kermarrec, Vincent Leroy, Afshin Moin, Christopher Thraves
• Keywords: Distributed And Parallel Computing, OPODIS 2010, Random Walk, Random Walk Approach, Recommender Systems