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
TWinner: understanding news queries with geo-content using Twitter
Proceedings of the 6th Workshop on Geographic Information Retrieval
Why searchers switch: understanding and predicting engine switching rationales
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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In this paper, we propose a simple framework to characterize the switching behavior between search engines based on click streams. We segment users into a number of categories based on their search engine usage during two adjacent time periods and construct the transition probability matrix across these usage categories. The principal eigenvector of the transposed transition probability matrix represents the limiting probabilities, which are proportions of users in each usage category at steady state. We experiment with this framework using click streams focusing on two search engines: one with a large market share and the other with a small market share. The results offer interesting insights into search engine switching. The limiting probabilities provide empirical evidence that small engines can still retain its fair share of users over time.