With the rapid growth of web search in recent years the problem of
modeling its users has started to attract more and more attention
of the information retrieval community. This has several
motivations. By building a model of user behavior we are
essentially developing a better understanding of a user, which
ultimately helps us to deliver a better search experience. A model
of user behavior can also be used as a predictive device for
non-observed items such as document relevance, which makes it
useful for improving search result ranking. Finally, in many
situations experimenting with real users is just infeasible and
hence user simulations based on accurate models play an essential
role in understanding the implications of algorithmic changes to
search engine results or presentation changes to the search engine
result page. In this survey we summarize advances in modeling user
click behavior on a web search engine result page. We present
simple click models as well as more complex models aimed at
capturing non-trivial user behavior patterns on modern search
engine result pages. We discuss how these models compare to each
other, what challenges they have, and what ways there are to
address these challenges. We also study the problem of evaluating
click models and discuss the main applications of click models.
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