Constructing click models for search users
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Search has reached a level at which a good understanding of user interactions may significantly impact its quality. Among all kinds of user interactions, click-through behavior on search results is an important one that attracted much attention. Clicking a certain result (or advertisement, or query suggestions, etc.) is usually regarded as an implicit feedback signal for its relevance, which is, however, very noisy. To understand if and how much a user click on a result document implies true relevance, one has to take into account different factors (usually named behavior biases), in addition to the factor of relevance, that may affect user click behaviors. Joachims et al. (2005) worked on extracting reliable implicit feedback from user behaviors, and concluded that click logs are informative yet biased. Previous studies revealed several bias aspects such as “position” (Craswell et al. 2008), “trust” (O’Brien and Keane 2006) and “presentation” (Wang et al. 2013) factors....
KeywordsDynamic Bayesian Network Mouse Movement Implicit Feedback Query Suggestion Search Session
We thank all authors for their contributions and allowing us to review their works for the special issue. We also want to thank our reviewers and the editors-in-chief Prof. Tetsuya Sakai and Prof. Charles Clarke for their great help and support in organizing this issue.
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