On-line Learning and the Metrical Task System Problem
Machine Learning
Competition between Internet Search Engines
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 8 - Volume 8
Evaluating implicit measures to improve web search
ACM Transactions on Information Systems (TOIS)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Investigating the querying and browsing behavior of advanced search engine users
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Defection detection: predicting search engine switching
Proceedings of the 17th international conference on World Wide Web
Enhancing web search by promoting multiple search engine use
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
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
Characterizing and predicting search engine switching behavior
Proceedings of the 18th ACM conference on Information and knowledge management
An analysis of search engine switching behavior using click streams
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Why searchers switch: understanding and predicting engine switching rationales
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Effects of search success on search engine re-use
Proceedings of the 20th ACM international conference on Information and knowledge management
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
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Search engines are key components in the online world and the choice of search engine is an important determinant of the user experience In this work we seek to model user behaviors and determine key variables that affect search engine usage In particular, we study the engine usage behavior of more than ten thousand users over a period of six months and use machine learning techniques to identify key trends in the usage of search engines and their relationship with user satisfaction We also explore methods to determine indicators that are predictive of user trends and show that accurate predictive user models of search engine usage can be developed Our findings have implications for users as well as search engine designers and marketers seeking to better understand and retain their users.