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)
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Finding Similar Queries to Satisfy Searches Based on Query Traces
OOIS '02 Proceedings of the Workshops on Advances in Object-Oriented Information Systems
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Semantic similarity between search engine queries using temporal correlation
WWW '05 Proceedings of the 14th international conference on World Wide Web
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Concept-based interactive query expansion
Proceedings of the 14th ACM international conference on Information and knowledge management
A web-based kernel function for measuring the similarity of short text snippets
Proceedings of the 15th international conference on World Wide Web
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Improving search engines by query clustering
Journal of the American Society for Information Science and Technology
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Personalized Concept-Based Clustering of Search Engine Queries
IEEE Transactions on Knowledge and Data Engineering
Query suggestions using query-flow graphs
Proceedings of the 2009 workshop on Web Search Click Data
Patterns of query reformulation during Web searching
Journal of the American Society for Information Science and Technology
The Application of Association Rules Algorithm on Web Search Engine
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 02
CONQUER: a system for efficient context-aware query suggestions
Proceedings of the 20th international conference companion on World wide web
A Survey of Automatic Query Expansion in Information Retrieval
ACM Computing Surveys (CSUR)
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Query Recommendation for Improving Search Engine Results
International Journal of Information Retrieval Research
Concept based query recommendation
AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference - Volume 121
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The information explosion on the Internet has placed high demands on search engines. Despite the improvements in search engine technology, the precision of current search engines is still unsatisfactory. Moreover, the queries submitted by users are short, ambiguous and imprecise. This leads to a number of problems in dealing with similar queries. The problems include lack of common keywords, selection of different documents by the search engine and lack of common clicks etc. These problems render the traditional query clustering methods unsuitable for query recommendations. In this paper, we propose a new query recommendation system. For this, we have identified conceptually related queries by capturing users' preferences using click-through graphs of web search logs and by extracting the best features, relevant to the queries, from the snippets. The proposed system has an online feature extraction phase and an offline phase in which feature filtering and query clustering are performed. Query clustering is carried out by a new tripartite agglomerative clustering algorithm, Query-Document-Concept Clustering, in which the documents are used innovatively to decouple queries and features/concepts in a tripartite graph structure. This results in clusters of similar queries, associated clusters of documents and clusters of features. We model the query recommendation problem in four different ways. Two models are non-personalized and personalized content-ignorant models. Other two are non-personalized and personalized content-aware models. Three similarity measures are introduced to estimate different kinds of similarities. Experimental results show that the proposed approach has better precision, recall and F-measure than the existing approaches.