Patterns of search: analyzing and modeling Web query refinement
UM '99 Proceedings of the seventh international conference on User modeling
Analysis of a very large web search engine query log
ACM SIGIR Forum
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
Extracting query modifications from nonlinear SVMs
Proceedings of the 11th international conference on World Wide Web
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Generalized Query Patterns from Web Logs
HICSS '01 Proceedings of the 34th Annual Hawaii International Conference on System Sciences ( HICSS-34)-Volume 5 - Volume 5
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Textual information retrieval with user profiles using fuzzy clustering and inferencing
Intelligent exploration of the web
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Analysis of the query logs of a web site search engine
Journal of the American Society for Information Science and Technology
Experiments on query expansion for internet yellow page services using web log mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Recommendation system based on the clustering of frequent sets
WSEAS Transactions on Information Science and Applications
Data Mining and Knowledge Discovery
Expert Systems with Applications: An International Journal
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This paper presents a simple and intuitive method for mining search engine query logs for fast social filtering, where searchers are provided with dynamic query recommendations on a large-scale industrial-strength search engine. We adopt a dynamic approach that is able to absorb new and recent trends in web usage trends on search engines, while forgetting outdated trends, thus adapting to dynamic changes in web user's interests. In order to get well-rounded recommendations, we combine two methods: first, we model search engine users'sequential search behavior, and interpret this consecutive search behavior as client-side query refinement, that should form the basis for the search engine's own query refinement process. This query refinement process is exploited to learn useful information that helps generate related queries. Second, we combine this method with a traditional text or content based similarity method to compensate for the shortness of query sessions and sparsity of real query log data.