  Click-through data has proven to be a valuable resource for improving
search-ranking quality. Search engines can easily collect click data, but
biases introduced in the data can make it difficult to use the data
effectively. In order to measure the effects of biases, many click models have
been proposed in the literature. However, none of the models can explain the
observation that users with different search intent (e.g., informational,
navigational, etc.) have different click behaviors. In this paper, we study how
differences in user search intent can influence click activities and determined
that there exists a bias between user search intent and the relevance of the
document relevance. Based on this observation, we propose a search intent bias
hypothesis that can be applied to most existing click models to improve their
ability to learn unbiased relevance. Experimental results demonstrate that
after adopting the search intent hypothesis, click models can better interpret
user clicks and substantially improve retrieval performance.
