IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
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
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery 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
Context-aware query classification
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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
Predicting short-term interests using activity-based search context
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
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
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
A novel model for user clicks identification based on hidden semi-Markov
Journal of Network and Computer Applications
Incorporating vertical results into search click models
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
User model-based metrics for offline query suggestion evaluation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Click model-based information retrieval metrics
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Fighting search engine amnesia: reranking repeated results
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Exploiting contextual factors for click modeling in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
Estimating ad group performance in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
User modeling in search logs via a nonparametric bayesian approach
Proceedings of the 7th ACM international conference on Web search and data mining
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Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session. Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model~(TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.