Patterns of search: analyzing and modeling Web query refinement
UM '99 Proceedings of the seventh international conference on User modeling
Combining evidence for automatic web session identification
Information Processing and Management: an International Journal - Issues of context in information retrieval
ACM SIGIR Forum
Display time as implicit feedback: understanding task effects
Proceedings of the 27th 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
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
Automatic new topic identification using multiple linear regression
Information Processing and Management: an International Journal
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
Mining long-term search history to improve search accuracy
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
Information re-retrieval: repeat queries in Yahoo's logs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Models of searching and browsing: languages, studies, and applications
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Click chain model in web search
Proceedings of the 18th international conference on World wide web
In the Mood to Click? Towards Inferring Receptiveness to Search Advertising
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
An analysis framework for search sequences
Proceedings of the 18th ACM conference on Information and knowledge management
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
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Towards predicting web searcher gaze position from mouse movements
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Exploring searcher interactions for distinguishing types of commercial intent
Proceedings of the 19th international conference on World wide web
Ready to buy or just browsing?: detecting web searcher goals from interaction data
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
The anatomy of a click: modeling user behavior on web information systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Who uses web search for what: and how
Proceedings of the fourth ACM international conference on Web search and data mining
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
Evaluating new search engine configurations with pre-existing judgments and clicks
Proceedings of the 20th international conference on World wide web
Modeling and analysis of cross-session search tasks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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
A semi-supervised approach to modeling web search satisfaction
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Enabling direct interest-aware audience selection
Proceedings of the 21st ACM international conference on Information and knowledge management
Predicting web search success with fine-grained interaction data
Proceedings of the 21st ACM international conference on Information and knowledge management
Personalized models of search satisfaction
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
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Mining user web search activity potentially has a broad range of applications including web result pre-fetching, automatic search query reformulation, click spam detection, estimation of document relevance and prediction of user satisfaction. This analysis is difficult because the data recorded by search engines while users interact with them, although abundant, is very noisy. In this work, we explore the utility of mining search behavior of users, represented by observed variables including the time the user spends on the page, and whether the user reformulated his or her query. As a case study, we examine the contribution this data makes to predicting the relevance of a document in the absence of document content models. To this end, we first propose a method for grouping the interactions of a particular user according to the different tasks he or she undertakes. With each task corresponding to a distinct information need, we then propose a Bayesian Network to holistically model these interactions. The aim is to identify distinct patterns of search behaviors. Finally, we join these patterns to a list of custom features and we use gradient boosted decision trees to predict the relevance of a set of query document pairs for which we have relevance assessments. The experimental results confirm the potential of our model, with significant improvements in precision for predicting the relevance of documents based on a model of the user's search and click behavior, over a baseline model using only click and query features, with no Bayesian Network input.