Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
Expectation Propagation for approximate Bayesian inference
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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
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
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
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
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
Click chain model in web search
Proceedings of the 18th international conference on World wide web
BBM: bayesian browsing model from petabyte-scale data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
User browsing models: relevance versus examination
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning click models via probit bayesian inference
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
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
Beyond ten blue links: enabling user click modeling in federated web search
Proceedings of the fifth ACM international conference on Web search and data mining
Search intent estimation from user's eye movements for supporting information seeking
Proceedings of the International Working Conference on Advanced Visual Interfaces
Explicit relevance models in intent-oriented information retrieval diversification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Learning to rank social update streams
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Modeling browsing behavior for click analysis in sponsored search
Proceedings of the 21st ACM international conference on Information and knowledge management
Using intent information to model user behavior in diversified search
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Predicting query reformulation type from user behavior
Proceedings of the 28th Annual ACM Symposium on Applied Computing
A general evaluation measure for document organization tasks
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Incorporating user preferences into click models
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Modeling clicks beyond the first result page
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Mining search and browse logs for web search: A Survey
ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Session modeling to predict online buyer behavior
Proceedings of the 2013 workshop on Data-driven user behavioral modelling and mining from social media
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Modeling a user's click-through behavior in click logs is a challenging task due to the well-known position bias problem. Recent advances in click models have adopted the examination hypothesis which distinguishes document relevance from position bias. In this paper, we revisit the examination hypothesis and observe that user clicks cannot be completely explained by relevance and position bias. Specifically, users with different search intents may submit the same query to the search engine but expect different search results. Thus, there might be a bias between user search intent and the query formulated by the user, which can lead to the diversity in user clicks. This bias has not been considered in previous works such as UBM, DBN and CCM. In this paper, we propose a new intent hypothesis as a complement to the examination hypothesis. This hypothesis is used to characterize the bias between the user search intent and the query in each search session. This hypothesis is very general and can be applied to most of the existing click models to improve their capacities in learning unbiased relevance. Experimental results demonstrate that after adopting the intent hypothesis, click models can better interpret user clicks and achieve a significant NDCG improvement.