Building a click model: From idea to practice

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Click-through information is considered as a valuable source of users' implicit relevance feedback. As user behavior is usually influenced by a number of factors such as position, presentation style and site reputation, researchers have proposed a variety of assumptions to generate a reasonable estimation of result relevance. Therefore, many click models have been proposed to describe how user click action happens and to predict click probability (and search result relevance). This work builds upon many years of existing efforts from THUIR labs, summarizes the most recent advances and provides a series of practical click models. In this paper, we give an introduction of how to build an effective click model. We use two click models as specific examples to introduce the general procedures of building a click model. We also introduce common evaluation metrics for the comparison of different click models. Some useful datasets and tools are also introduced to help readers better understand and implement existing click models. The goal of this survey is to bring together current efforts in the area, summarize the research performed so far and give a view on building click models for web search.