http://www2009.eprints.org/4/
Fast Dynamic Reranking in Large Graphs
In this paper we consider the problem of re-ranking search results by incorporating user feedback. We present a graph theoretic measure for discriminating irrelevant results from relevant results using a few labeled examples provided by the user. The key intuition is that nodes relatively closer (in graph topology) to the relevant nodes than the irrelevant nodes are more likely to be relevant. We present a simple sampling algorithm to evaluate this measure at speciﬁc nodes of interest, and an efficient branch and bound algorithm to compute the top k nodes from the entire graph under this measure. On quantiﬁable prediction tasks the introduced measure outperforms other diffusion-based proximity measures which take only the positive relevance feedback into account. On the Entity-Relation graph built from the authors and papers of the entire DBLP citation corpus (1.4 million nodes and 2.2 million edges) our branch and bound algorithm takes about 1.5 seconds to retrieve the top 10 nodes w.r.t. this measure with 10 labeled nodes.
Sarkar, Purnamrita
Moore, Andrew W.
Fast Dynamic Reranking in Large Graphs
2009-04-06T19:08:38Z
Sarkar, Purnamrita <http://www2009.eprints.org/view/author/Sarkar=3APurnamrita=3A=3A.html> and Moore, Andrew W. <http://www2009.eprints.org/view/author/Moore=3AAndrew_W=2E=3A=3A.html> (2009) Fast Dynamic Reranking in Large Graphs. In: 18th International World Wide Web Conference, April 20th-24th, 2009, Madrid, Spain.
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