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
Deriving concept hierarchies from text
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Automatic query wefinement using lexical affinities with maximal information gain
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical approach to term suggestion device
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Using terminological feedback for web search refinement: a log-based study
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Building and applying a concept hierarchy representation of a user profile
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Re-examining the potential effectiveness of interactive query expansion
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
A study of interface support mechanisms for interactive information retrieval
Journal of the American Society for Information Science and Technology - Research Articles
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Studying the use of popular destinations to enhance web search interaction
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Query suggestion based on user landing pages
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Extracting semantic relations from query logs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
How does clickthrough data reflect retrieval quality?
Proceedings of the 17th ACM conference on Information and knowledge management
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Query reformulation using anchor text
Proceedings of the third ACM international conference on Web search and data mining
Query similarity by projecting the query-flow graph
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
AutoEval: an evaluation methodology for evaluating query suggestions using query logs
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Enriching query flow graphs with click information
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
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Query recommendation is becoming a common feature of web search engines especially those for Intranets where the context is more restrictive. This is because of its utility for supporting users to find relevant information in less time by using the most suitable query terms. Selection of queries for recommendation is typically done by mining web documents or search logs of previous users. We propose the integration of these approaches by combining two models namely the concept hierarchy, typically built from an Intranet's documents, and the query flow graph, typically built from search logs. However, we build our concept hierarchy model from terms extracted from a subset (training set) of search logs since these are more representative of the user view of the domain than any concepts extracted from the collection. We then continually adapt the model by incorporating query refinements from another subset (test set) of the user search logs. This process implies learning from or reusing previous users' querying experience to recommend queries for a new but similar user query. The adaptation weights are extracted from a query flow graph built with the same logs. We evaluated our hybrid model using documents crawled from the Intranet of an academic institution and its search logs. The hybrid model was then compared to a concept hierarchy model and query flow graph built from the same collection and search logs respectively. We also tested various strategies for combining information in the search logs with respect to the frequency of clicked documents after query refinement. Our hybrid model significantly outperformed the concept hierarchy model and query flow graph when tested over two different periods of the academic year. We intend to further validate our experiments with documents and search logs from another institution and devise better strategies for selecting queries for recommendation from the hybrid model.