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
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
Defection detection: predicting search engine switching
Proceedings of the 17th international conference on World Wide Web
Stream prediction using a generative model based on frequent episodes in event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
AUC: a statistically consistent and more discriminating measure than accuracy
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Characterizing and predicting search engine switching behavior
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
How does search behavior change as search becomes more difficult?
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Predicting searcher frustration
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Parallel boosted regression trees for web search ranking
Proceedings of the 20th international conference on World wide web
Why searchers switch: understanding and predicting engine switching rationales
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
Proceedings of the 20th ACM international conference on Information and knowledge management
Scikit-learn: Machine Learning in Python
The Journal of Machine Learning Research
Personalized click model through collaborative filtering
Proceedings of the fifth ACM international conference on Web search and data mining
Large-scale analysis of individual and task differences in search result page examination strategies
Proceedings of the fifth ACM international conference on Web search and data mining
Probabilistic models for personalizing web search
Proceedings of the fifth ACM international conference on Web search and data mining
Modeling long-term search engine usage
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Modeling the impact of short- and long-term behavior on search personalization
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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
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Sometimes, during a search task users may switch from one search engine to another for several reasons, e.g., dissatisfaction with the current search results or desire for broader topic coverage. Detecting the fact of switching is difficult but important for understanding users' satisfaction with the search engine and the complexity of their search tasks, leading to economic significance for search providers. Previous research on switching detection mainly focused on studying different signals useful for the task and particular reasons for switching. Although it is known that switching is a personal choice of a user and different users have different search behavior, little has been done to understand how these differences could be used for switching detection. In this paper we study the effectiveness of learning personal behavior patterns for switching detection and present a personalized approach which uses user's session history containing sessions with and without switches. Experiments show that users' personal habits and behavior patterns are indeed among the most informative signals. Our findings can be used by a search log analyzer for engine switching detection and potentially other log mining problems, thus providing valuable signals for search providers to improve user experience.