A language modeling approach to information retrieval
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
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
Methods for comparing rankings of search engine results
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Random walks on the click graph
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The relationship between IR effectiveness measures and user satisfaction
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Discovering key concepts in verbose queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Exploring Query Formulation and Reformulation: A Preliminary Study to Map Users' Search Behaviour
ECDL '08 Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries
Are click-through data adequate for learning web search rankings?
Proceedings of the 17th ACM conference on Information and knowledge management
Online expansion of rare queries for sponsored search
Proceedings of the 18th international conference on World wide web
Smoothing clickthrough data for web search ranking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Analyzing and evaluating query reformulation strategies in web search logs
Proceedings of the 18th ACM conference on Information and knowledge management
Context sensitive synonym discovery for web search queries
Proceedings of the 18th ACM conference on Information and knowledge management
Generalized distances between rankings
Proceedings of the 19th international conference on World wide web
Extending average precision to graded relevance judgments
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Web search solved?: all result rankings the same?
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Reverted indexing for feedback and expansion
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Adapting document ranking to users’ preferences using click-through data
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
Applying associative relationship on the clickthrough data to improve web search
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
Analyzing, Detecting, and Exploiting Sentiment in Web Queries
ACM Transactions on the Web (TWEB)
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It is well known that clickthrough data can be used to improve the effectiveness of search results: broadly speaking, a query's past clicks are a predictor of future clicks on documents. However, when a new or unusual query appears, or when a system is not as widely used as a mainstream web search system, there may be little to no click data available to improve the results. Existing methods to boost query performance for sparse queries extend the query-document click relationship to more documents or queries, but require substantial clickthrough data from other queries. In this work we describe a way to boost rarely-clicked queries in a system where limited clickthrough data is available for all queries. We describe a probabilistic approach for carrying out that estimation and use it to rerank retrieved documents. We utilize information from co-click queries, subset queries, and synonym queries to estimate the clickthrough for a sparse query. Our experiments on a query log from a medical informatics company demonstrate that when overall clickthrough data is sparse, reranking search results using clickthrough information from related queries significantly outperforms reranking that employs clickthrough information from the query alone.