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WWW '99 Proceedings of the eighth international conference on World Wide Web
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
The worst-case time complexity for generating all maximal cliques and computational experiments
Theoretical Computer Science - Computing and combinatorics
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Proceedings of the third ACM conference on Recommender systems
Exploiting query reformulations for web search result diversification
Proceedings of the 19th international conference on World wide web
Detecting epidemic tendency by mining search logs
Proceedings of the 19th international conference on World wide web
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Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Towards bipartite graph data management
CloudDB '10 Proceedings of the second international workshop on Cloud data management
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Proceedings of the fourth ACM international conference on Web search and data mining
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
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Proceedings of the 20th ACM international conference on Information and knowledge management
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AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paper we describe a problem of discovering query clusters from a click-through graph of web search logs. The graph consists of a set of web search queries, a set of pages selected for the queries, and a set of directed edges that connects a query node and a page node clicked by a user for the query. The proposed method extracts all maximal bipartite cliques (bicliques) from a click-through graph and compute an equivalence set of queries (i.e., a query cluster) from the maximal bicliques. A cluster of queries is formed from the queries in a biclique. We present a scalable algorithm that enumerates all maximal bicliques from the click-through graph. We have conducted experiments on Yahoo web search queries and the result is promising.