Modeling a user's click-through behavior in click logs is a challenging task due to the well-known position bias problem. Recent advances in click models have adopted the examination hypothesis which distinguishes the document relevance from the position bias. In this paper, we revisit the examination hypothesis and observe that user clicks cannot be completely explained by the relevance and the position bias. Specifically, users with different search intents may submit the same query to the

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... rch engine while expecting different search results. Thus, there might be a bias between user search intent and the query formulated by the user, which leads to the diversity in user clicks. This bias has not been considered in previous works such as UBM, DBN and CCM. In this paper, we propose a new intent hypothesis as a complement to the examination hypothesis. This hypothesis is used to characterize the bias between the user search intent and the query at each search session. This hypothesis is very general and can be applied to most of the existing click models to improve their capacities in learning unbiased relevance. Experimental results demonstrate that after adopting the intent hypothesis, click models can better interpret user clicks and achieve a significant NDCG improvement.