Article

Recommendation via Query Centered Random Walk on K-Partite Graph

Authors:

Haibin Cheng,

Pang-Ning Tan,

Jon Sticklen,

William F. PunchAuthors Info & Claims

ICDM '07: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining

Pages 457 - 462

https://doi.org/10.1109/ICDM.2007.8

Published: 28 October 2007 Publication History

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Abstract

This paper presents an algorithm for recommending items using a diverse set of features. The items are recommended by performing a random walk on the k-partite graph constructed from the heterogenous features. To support personalized recommendation, the random walk must be initiated separately for each user, which is computationally demanding given the massive size of the graph. To overcome this problem, we apply multi-way clustering to group together the highly correlated nodes. A recommendation is then made by traversing the subgraph induced by clusters associated with a user's interest. Our experimental results on real data sets demonstrate the efficacy of the proposed algorithm.

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Li LYao YTang JFan WTong HKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)QUINTProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939768(985-994)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939768
Pei YChakraborty NSycara K(2015)Nonnegative matrix tri-factorization with graph regularization for community detection in social networksProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832415.2832538(2083-2089)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832415.2832538
Lee SKahng MLee S(2015)Constructing compact and effective graphs for recommender systems via node and edge aggregationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.11.06242:7(3396-3409)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.11.062
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Index Terms

Recommendation via Query Centered Random Walk on K-Partite Graph
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

Index terms have been assigned to the content through auto-classification.

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Published In

ICDM '07: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining

October 2007

767 pages

ISBN:0769530184

Publisher

IEEE Computer Society

United States

Publication History

Published: 28 October 2007

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Li LYao YTang JFan WTong HKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)QUINTProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939768(985-994)Online publication date: 13-Aug-2016
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Pei YChakraborty NSycara K(2015)Nonnegative matrix tri-factorization with graph regularization for community detection in social networksProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832415.2832538(2083-2089)Online publication date: 25-Jul-2015
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Lee SKahng MLee S(2015)Constructing compact and effective graphs for recommender systems via node and edge aggregationsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.11.06242:7(3396-3409)Online publication date: 1-May-2015
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Huang QChen BWang JMei T(2014)Personalized Video Recommendation through Graph PropagationACM Transactions on Multimedia Computing, Communications, and Applications10.1145/259877910:4(1-17)Online publication date: 4-Jul-2014
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Chiang MLiou JWang JPeng WShan M(2013)Exploring heterogeneous information networks and random walk with restart for academic searchKnowledge and Information Systems10.1007/s10115-012-0523-836:1(59-82)Online publication date: 1-Jul-2013
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Mandayam Comar PTan PJain A(2012)A framework for joint community detection across multiple related networksNeurocomputing10.1016/j.neucom.2011.04.03576:1(93-104)Online publication date: 1-Jan-2012
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