Constructing click models for search users

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. Recently, we have also witnessed the rising of ranking models which rely on click-through data as a biased noisy information source for training purposes (Wang et al. 2016; Joachims et al. 2017). Several click models (e.g. Dupret and Piwowarski 2008; Chapelle and Zhang 2009; Guo et al. 2009) have been proposed, which usually involve additional events (e.g. examination) and different assumptions. These models are designed to eliminate the effects of various behavior biases (e.g. position bias, presentation bias, trust bias, etc.) to provide a better estimation of result relevance. Many of these efforts have been adopted to generate useful ranking signals for production rankers of commercial search engines.