Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGIR Forum
Eye-tracking analysis of user behavior in WWW search
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
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
Evaluating the accuracy of implicit feedback from clicks and query reformulations in Web search
ACM Transactions on Information Systems (TOIS)
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
A regression framework for learning ranking functions using relative relevance judgments
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Global ranking by exploiting user clicks
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Temporal query log profiling to improve web search ranking
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
User behavior driven ranking without editorial judgments
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Online learning for recency search ranking using real-time user feedback
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Expected browsing utility for web search evaluation
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A search log-based approach to evaluation
ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
Learning to re-rank web search results with multiple pairwise features
Proceedings of the fourth ACM international conference on Web search and data mining
Click-graph modeling for facet attribute estimation of web search queries
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
Addressing people's information needs directly in a web search result page
Proceedings of the 20th international conference on World wide web
Proceedings of the 20th international conference on World wide web
Learning to re-rank: query-dependent image re-ranking using click data
Proceedings of the 20th international conference on World wide web
Find it if you can: a game for modeling different types of web search success using interaction data
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 20th ACM international conference on Information and knowledge management
A noise-aware click model for web search
Proceedings of the fifth ACM international conference on Web search and data mining
Personalized click model through collaborative filtering
Proceedings of the fifth ACM international conference on Web search and data mining
Fair and balanced: learning to present news stories
Proceedings of the fifth ACM international conference on Web search and data mining
Beyond ten blue links: enabling user click modeling in federated web search
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Feedback in context: supporting the evolution of IT-Ecosystems
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Multi-objective optimization for sponsored search
Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy
An Online Learning Framework for Refining Recency Search Results with User Click Feedback
ACM Transactions on Information Systems (TOIS)
Estimating interleaved comparison outcomes from historical click data
Proceedings of the 21st ACM international conference on Information and knowledge management
More than relevance: high utility query recommendation by mining users' search behaviors
Proceedings of the 21st ACM international conference on Information and knowledge management
Fighting search engine amnesia: reranking repeated results
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Search engine switching detection based on user personal preferences and behavior patterns
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Captions and biases in diagnostic search
ACM Transactions on the Web (TWEB)
Click-boosting random walk for image search reranking
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Beyond clicks: query reformulation as a predictor of search satisfaction
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
User intent and assessor disagreement in web search evaluation
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Robust models of mouse movement on dynamic web search results pages
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Fidelity, Soundness, and Efficiency of Interleaved Comparison Methods
ACM Transactions on Information Systems (TOIS)
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We propose a new model to interpret the clickthrough logs of a web search engine. This model is based on explicit assumptions on the user behavior. In particular, we draw conclusions on a document relevance by observing the user behavior after he examined the document and not based on whether a user clicks or not a document url. This results in a model based on intrinsic relevance, as opposed to perceived relevance. We use the model to predict document relevance and then use this as feature for a "Learning to Rank" machine learning algorithm. Comparing the ranking functions obtained by training the algorithm with and without the new feature we observe surprisingly good results. This is particularly notable given that the baseline we use is the heavily optimized ranking function of a leading commercial search engine. A deeper analysis shows that the new feature is particularly helpful for non navigational queries and queries with a large abandonment rate or a large average number of queries per session. This is important because these types of query is considered to be the most difficult to solve.