SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
AQUAM: automatic query formulation architecture for mobile applications
Proceedings of the 7th International Conference on Mobile and Ubiquitous Multimedia
Using word-sense disambiguation methods to classify web queries by intent
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
First query term extraction from current webpage for mobile applications
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Axiomatic ranking of network role similarity
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Rewriting null e-commerce queries to recommend products
Proceedings of the 21st international conference companion on World Wide Web
E-rank: A Structural-Based Similarity Measure in Social Networks
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
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We focus on the problem of query rewriting for sponsored search. We base rewrites on a historical click graph that records the ads that have been clicked on in response to past user queries. Given a query q, we first consider Simrank [2] as a way to identify queries similar to q, i.e., queries whose ads a user may be interested in. We argue that Simrank fails to properly identify query similarities in our application, and we present two enhanced versions of Simrank: one that exploits weights on click graph edges and another that exploits evidence." We experimentally evaluate our new schemes against Simrank, using actual click graphs and queries form Yahoo!, and using a variety of metrics. Our results show that the enhanced methods can yield more and better query rewrites.