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
An analysis of search engine switching behavior using click streams
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Investigating behavioral variability in web search
Proceedings of the 16th international conference on World Wide Web
Models of searching and browsing: languages, studies, and applications
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
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
Modeling long-term search engine usage
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
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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Searchers have a choice about which Web search engine they use when looking for information online. If they are unsuccessful on one engine, users may switch to a different engine to continue their search. By predicting when switches are likely to occur, the search experience can be modified to retain searchers or ensure a quality experience for incoming searchers. In this poster, we present research on a technique for predicting search engine switches. Our findings show that prediction is possible at a reasonable level of accuracy, particularly when personalization or user grouping is employed. These findings have implications for the design of applications to support more effective online searching.