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A Recommender System Based on Local Random Walks and Spectral Methods

  • Zeinab Abbassi
  • Vahab S. Mirrokni
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5439)

Abstract

In this paper, we design recommender systems for blogs based on the link structure among them. We propose algorithms based on refined random walks and spectral methods. First, we observe the use of the personalized page rank vector to capture the relevance among nodes in a social network. We apply the local partitioning algorithms based on refined random walks to approximate the personalized page rank vector, and extend these ideas from undirected graphs to directed graphs. Moreover, inspired by ideas from spectral clustering, we design a similarity metric among nodes of a social network using the eigenvalues and eigenvectors of a normalized adjacency matrix of the social network graph. In order to evaluate these algorithms, we crawled a set of blogs and construct a blog graph. We expect that these algorithms based on the link structure perform very well for blogs, since the average degree of nodes in the blog graph is large. Finally, we compare the performance of our algorithms on this data set. In particular, the acceptable performance of our algorithms on this data set justifies the use of a link-based recommender system for social networks with large average degree.

Keywords

Random Walk Directed Graph Recommender System Spectral Cluster Link Structure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Zeinab Abbassi
    • 1
  • Vahab S. Mirrokni
    • 2
  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada
  2. 2.Microsoft ResearchRedmondUSA

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