A cognitive view of the situational dynamism of user-centered relevance estimation
Journal of the American Society for Information Science - Special issue: relevance research
Long-Term Learning for Web Search Engines
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The concept of relevance in IR
Journal of the American Society for Information Science and Technology
Engineering a multi-purpose test collection for web retrieval experiments
Information Processing and Management: an International Journal
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
User performance versus precision measures for simple search tasks
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
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
Active exploration for learning rankings from clickthrough data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
BBM: bayesian browsing model from petabyte-scale data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Are Clickthroughs Useful for Image Labelling?
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Post-rank reordering: resolving preference misalignments between search engines and end users
Proceedings of the 18th ACM conference on Information and knowledge management
Bayesian Browsing Model: Exact Inference of Document Relevance from Petabyte-Scale Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning to re-rank: query-dependent image re-ranking using click data
Proceedings of the 20th international conference on World wide web
Evaluating implicit judgments from image search clickthrough data
Journal of the American Society for Information Science and Technology
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User clicks--also known as clickthrough data--have been cited as an implicit form of relevance feedback. Previous work suggests that relative preferences between documents can be accurately derived from user clicks. In this paper, we analyze the impact of document reordering--based on clickthrough--on search effectiveness, measured using both TREC and user relevance judgments. We also propose new strategies for document reordering that can outperform current techniques. Preliminary results show that current reordering methods do not lead to consistent improvements of search quality, but may even lead to poorer results if not used with care.