Abstract
Recent advances in click model have positioned it as an effective approach to estimate document relevance based on user behavior in web search. Yet, few works have been conducted to explore the use of click model to help web search ranking. In this paper, we focus on learning a ranking function by taking the results from a click model into account. Thus, besides the editorial relevance data arising from the explicit manually labeled search result by experts, we also have the estimated relevance data that is automatically inferred from click models based on user search behavior. We carry out extensive experiments on large-scale commercial datasets and demonstrate the effectiveness of the proposed methods.
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