Lexical ambiguity and information retrieval
ACM Transactions on Information Systems (TOIS)
Word sense disambiguation for information retrieval
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The impact on retrieval effectiveness of skewed frequency distributions
ACM Transactions on Information Systems (TOIS)
Word sense disambiguation in information retrieval revisited
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Probabilistic structured query methods
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Identifying ambiguous queries in web search
Proceedings of the 16th international conference on World Wide Web
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Entropy of search logs: how hard is search? with personalization? with backoff?
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
To personalize or not to personalize: modeling queries with variation in user intent
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Learning query intent from regularized click graphs
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Ambiguous queries: test collections need more sense
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Understanding user's query intent with wikipedia
Proceedings of the 18th international conference on World wide web
Web search intent induction via automatic query reformulation
HLT-NAACL-Short '04 Proceedings of HLT-NAACL 2004: Short Papers
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A user behavior model for average precision and its generalization to graded judgments
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Expected browsing utility for web search evaluation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Query suggestions in the absence of query logs
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
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Understanding users' search intents is critical component of modern search engines. A key limitation made by most query log analyses is the assumption that each clicked web result represents one unique intent. However, there are many search tasks, such as comparison shopping or in-depth research, where a user's intent is to explore many documents. In these cases, the assumption of a one-to-one correspondence between clicked documents and user intent breaks down. To capture and understand such behaviors, we propose the use of click patterns. Click patterns capture the relationship among clicks on search results by treating the set of clicks made by a user as a single unit. We aggregate click patterns together using a hierarchical clustering algorithm to discover the common click patterns. By using click patterns as an empirical representation of user intent, we are able to create a rich representation of mixtures of multiple navigational and informational intents. We analyze real search logs and demonstrate that such complex mixtures of intents do occur in the wild and can be identified using click patterns. We further demonstrate the usefulness of click patterns by integrating them into a measure of query ambiguity and into a query recommendation task. We show that calculating query ambiguity as the entropy over the distribution of click patterns provides a measure of ambiguity with improved discriminative power, consistency and temporal stability as compared to previous measures of ambiguity. We explore the use of click pattern similarity and click pattern entropy in generating query recommendations and show promising results.