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Concepts embody the knowledge of the world and facilitate the cognitiveprocesses of human beings. Mining concepts from web documents and constructingthe corresponding taxonomy are core research problems in text understanding andsupport many downstream tasks such as query analysis, knowledge baseconstruction, recommendation, and search. However, we argue that most priorstudies extract formal and overly general concepts from Wikipedia or static webpages, which are not representing the user perspective. In this paper, wedescribe our experience of implementing and deploying ConcepT in Tencent QQBrowser. It discovers user-centered concepts at the right granularityconforming to user interests, by mining a large amount of user queries andinteractive search click logs. The extracted concepts have the propergranularity, are consistent with user language styles and are dynamicallyupdated. We further present our techniques to tag documents with user-centeredconcepts and to construct a topic-concept-instance taxonomy, which has helpedto improve search as well as news feeds recommendation in Tencent QQ Browser.We performed extensive offline evaluation to demonstrate that our approachcould extract concepts of higher quality compared to several other existingmethods. Our system has been deployed in Tencent QQ Browser. Results fromonline A/B testing involving a large number of real users suggest that theImpression Efficiency of feeds users increased by 6.01% after incorporating theuser-centered concepts into the recommendation framework of Tencent QQ Browser.

A User-Centered Concept Mining System for Query and Document Understanding at Tencent