Introduction to algorithms
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
On the reuse of past optimal queries
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic feedback using past queries: social searching?
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Improving the effectiveness of information retrieval with local context analysis
ACM Transactions on Information Systems (TOIS)
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 6th international conference on Intelligent user interfaces
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Using navigation data to improve IR functions in the context of web search
Proceedings of the tenth international conference on Information and knowledge management
Query clustering using user logs
ACM Transactions on Information Systems (TOIS)
On bipartite and multipartite clique problems
Journal of Algorithms
Principal Direction Divisive Partitioning
Data Mining and Knowledge Discovery
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Query expansion using random walk models
Proceedings of the 14th ACM international conference on Information and knowledge management
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Link-based similarity measures for the classification of Web documents
Journal of the American Society for Information Science and Technology
Mining dependency relations for query expansion in passage retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering Documents and Words Using Bipartite Isoperimetric Graph Partitioning
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Scaling up all pairs similarity search
Proceedings of the 16th international conference on World Wide Web
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th 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
Introduction to Information Retrieval
Introduction to Information Retrieval
An optimization framework for query recommendation
Proceedings of the third ACM international conference on Web search and data mining
Selecting related terms in query-logs using two-stage SimRank
Proceedings of the 20th ACM international conference on Information and knowledge management
Context-aware query recommendation by learning high-order relation in query logs
Proceedings of the 20th ACM international conference on Information and knowledge management
Query expansion using web access log files
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
Clustering of search engine keywords using access logs
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Divergence measures based on the Shannon entropy
IEEE Transactions on Information Theory
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Search engine users often encounter the difficulty of phrasing the precise query that could lead to satisfactory search results. Query recommendation is considered an effective assistant in enhancing keyword-based queries in search engines and Web search software. In this paper, we present a Query-URL Bipartite based query reCommendation approach, called QUBiC. It utilizes the connectivity of a query-URL bipartite graph to recommend related queries and can significantly improve the accuracy and effectiveness of personalized query recommendation systems comparing with the conventional pairwise similarity based approach. The main contribution of the QUBiC approach is its three-phase framework for personalized query recommendations. The first phase is the preparation of queries and their search results returned by a search engine, which generates a historical query-URL bipartite collection. The second phase is the discovery of similar queries by extracting a query affinity graph from the bipartite graph, instead of operating on the original bipartite graph directly using biclique-based approach or graph clustering. The query affinity graph consists of only queries as its vertices and its edges are weighted according to a query-URL vector based similarity (dissimilarity) measure. The third phase is the ranking of similar queries. We devise a novel rank mechanism for ordering the related queries based on the merging distances of a hierarchical agglomerative clustering (HAC). By utilizing the query affinity graph and the HAC-based ranking, we are able to capture the propagation of similarity from query to query by inducing an implicit topical relatedness between queries. Furthermore, the flexibility of the HAC strategy makes it possible for users to interactively participate in the query recommendation process, and helps to bridge the gap between the determinacy of actual similarity values and the indeterminacy of users' information needs, allowing the lists of related queries to be changed from user to user and query to query, thus adaptively recommending related queries on demand. Our experimental evaluation results show that the QUBiC approach is highly efficient and more effective compared to the conventional query recommendation systems, yielding about 13.3 % as the most improvement in terms of precision.