Foundations of statistical natural language processing
Foundations of statistical natural language processing
Helping people find what they don't know
Communications of the ACM
A critical examination of TDT's cost function
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology
Query word deletion prediction
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Semantic Log Analysis Based on a User Query Behavior Model
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Hourly analysis of a very large topically categorized web query log
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
SVD based Term Suggestion and Ranking System
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
The loquacious user: a document-independent source of terms for query expansion
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
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Recommending questions using the mdl-based tree cut model
Proceedings of the 17th international conference on World Wide Web
An analysis of queries intended to search information for children
Proceedings of the third symposium on Information interaction in context
Mining Concept Sequences from Large-Scale Search Logs for Context-Aware Query Suggestion
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning to suggest: a machine learning framework for ranking query suggestions
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
A vlHMM approach to context-aware search
ACM Transactions on the Web (TWEB)
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Searchers' difficulty in formulating effective queries for their information needs is well known. Analysis of search session logs shows that users often pose short, vague queries and then struggle with revising them. Interactive query expansion (users selecting terms to add to their queries) dramatically improves effectiveness and satisfaction. Suggesting relevant candidate expansion terms based on the initial query enables users to satisfy their information needs faster. We find that suggesting query phrases other users have found it necessary to add for a given query (mined from session logs) dramatically improves the quality of suggestions over simply using cooccurrence. However, this exacerbates the sparseness problem faced when mining short queries that lack features. To mitigate this, we tag query phrases with higher level topical categories to mine more general rules, finding that this enables us to make suggestions for approximately 10% more queries while maintaining an acceptable false positive rate.