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A dynamic bayesian network click model for web search ranking
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Inferring search behaviors using partially observable Markov (POM) model
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A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
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Proceedings of the fifth ACM international conference on Web search and data mining
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SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Predicting web search success with fine-grained interaction data
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Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Using CrowdLogger for in situ information retrieval system evaluation
Proceedings of the 2013 workshop on Living labs for information retrieval evaluation
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Web search behaviour studies, including eye-tracking studies of search result examination, have resulted in numerous insights to improve search result quality and presentation. Yet, eye tracking studies have been restricted in scale, due to the expense and the effort required. Furthermore, as the reach of the Web expands, it becomes increasingly important to understand how searchers around the world see and interact with the search results. To address both challenges, we introduce ViewSer, a novel methodology for performing web search examination studies remotely, at scale, and without requiring eye-tracking equipment. ViewSer operates by automatically modifying the appearance of a search engine result page, to clearly show one search result at a time as if through a "viewport", while partially blurring the rest and allowing the participant to move the viewport naturally with a computer mouse or trackpad. Remarkably, the resulting result viewing and clickthrough patterns agree closely with unrestricted viewing of results, as measured by eye-tracking equipment, validated by a study with over 100 participants. We also explore applications of ViewSer to practical search tasks, such as analyzing the search result summary (snip- pet) attractiveness, result re-ranking, and evaluating snippet quality. These experiments could have only be done previously by tracking the eye movements for a small number of subjects in the lab. In contrast, our study was performed with over 100 participants, allowing us to reproduce and extend previous findings, establishing ViewSer as a valuable tool for large-scale search behavior experiments.