Eliminating Search Intent Bias in Learning to Rank Clickthrough data has proven to be a valuable resource for improving searchranking 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.

Machine ComprehensionIncorporated Relevance Matching In current web search engines, the relevance between a query and web pages (i.e. documents) is measured by Text Matching (TM) models. The documents retrieved mainly focus on matching text queries themselves but fail to find target information towards the user intent (e.g. the direct answer to questionstyle queries). Thus the document containing target information that users want may not be ranked at the top 1 in the search result or even not be recalled. Besides, as voice search and voicepowered assistants are entering our life, queries tend to be long tail ones, which needs a search engine evolved into a higher level of semantic relevance matching. Therefore, it is necessary to build an intenttarget relevance matching model in modern search scenarios. This paper proposes a unified model of Machine Comprehensionincorporated Relevance Matching (MCRM). Totally, MCRM models how web users choose the relevant documents to read by observing the titles or summaries, and further look for target information from them. To accomplish that, we first formulate two tasks as Text Matching and Target Extracting. For learning each task, a Contextaugmented Matching network (ContMatch) and a Matchingfused machine Comprehension network (MatComprehend) are proposed. Then, they are integrated into an endtoend framework that can not only measure the semantic relevance but also extract the intentrelated target, by deeply comprehending the semantics hidden in queries and exploiting intenttarget relations between queries and documents. In MCRM, the two tasks are jointly learned by a multitask learning approach, where the semantic relevance measured by ContMatch, and the intenttarget relevance captured by MatComprehend, are combined and enhanced mutually for boosting the final performance. We conduct extensive experiments on realworld data. The experimental results demonstrate the superiority of MCRM against the stateoftheart relevance models.