Understanding the intent behind mobile information needs
Proceedings of the 14th international conference on Intelligent user interfaces
Proceedings of the 18th international conference on World wide web
Segment-level display time as implicit feedback: a comparison to eye tracking
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Beyond DCG: user behavior as a predictor of a successful search
Proceedings of the third ACM international conference on Web search and data mining
Predicting searcher frustration
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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
Can search systems detect users' task difficulty?: some behavioral signals
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
No clicks, no problem: using cursor movements to understand and improve search
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Leaving so soon?: understanding and predicting web search abandonment rationales
Proceedings of the 21st ACM international conference on Information and knowledge management
Exploring and predicting search task difficulty
Proceedings of the 21st ACM international conference on Information and knowledge management
Towards estimating web search result relevance from touch interactions on mobile devices
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Mining touch interaction data on mobile devices to predict web search result relevance
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
TellMyRelevance!: predicting the relevance of web search results from cursor interactions
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Predicting searcher success and satisfaction is a key problem in Web search, which is essential for automatic evaluating and improving search engine performance. This problem has been studied actively in the desktop search setting, but not specifically for mobile search, despite many known differences between the two modalities. As mobile devices become increasingly popular for searching the Web, improving the searcher experience on such devices is becoming crucially important. In this paper, we explore the possibility of predicting searcher success and satisfaction in mobile search with a smart phone. Specifically, we investigate client-side interaction signals, including the number of browsed pages, and touch screen-specific actions such as zooming and sliding. Exploiting this information with machine learning techniques results in nearly 80% accuracy for predicting searcher success -- significantly outperforming the previous models.