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Learning to rank networked entities (2006)

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by Alekh Agarwal
Venue:In KDD
Citations:29 - 3 self
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BibTeX

@INPROCEEDINGS{Agarwal06learningto,
    author = {Alekh Agarwal},
    title = {Learning to rank networked entities},
    booktitle = {In KDD},
    year = {2006},
    pages = {14--23},
    publisher = {ACM Press}
}

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Abstract

Several algorithms have been proposed to learn to rank entities modeled as feature vectors, based on relevance feedback. However, these algorithms do not model network connections or relations between entities. Meanwhile, Pagerank and variants find the stationary distribution of a reasonable but arbitrary Markov walk over a network, but do not learn from relevance feedback. We present a framework for ranking networked entities based on Markov walks with parameterized conductance values associated with the network edges. We propose two flavors of conductance learning problems in our framework. In the first setting, relevance feedback comparing node-pairs hints that the user has one or more hidden preferred communities with large edge conductance, and the algorithm must discover these communities. We present a constrained maximum entropy network flow formulation whose dual can be solved efficiently using a cutting-plane approach and a quasi-Newton optimizer. In the second setting, edges have types, and relevance feedback hints that each edge type has a potentially different conductance, but this is fixed across the whole network. Our algorithm learns the conductances using an approximate Newton method.

Citations

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124 The intelligent surfer: Probabilistic combination of link and content information in PageRank - Richardson, Domingos - 2002
90 R-MAT: A recursive model for graph mining - Chakrabarti, Zhan, et al. - 2004
55 Support vector learning for ordinal regression - Herbrich, Graepel, et al. - 1999
38 Performance guarantees for regularized maximum entropy density estimation - Dudík, Phillips, et al. - 2004
27 A new paradigm for ranking pages on the World Wide Web - Tomlin
21 Authoritybased keyword queries in databases using ObjectRank - Balmin, Hristidis, et al. - 2004
18 A Limited Memory Variable Metric Method in Subspaces and Bound Constrained Optimization Problems - Benson, Moré - 2001
18 Creating customized authority lists - Chang, Cohn, et al. - 2000
16 Learning web page scores by error back-propagation - Diligenti, Gori, et al.
2 SemRank: Ranking complex semantic relationship search results on the semantic Web - Anywanwu, Maduko, et al. - 2005
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