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The problem of learning user search intents has attracted intensive attention from both industry and academia. However, state-of-the-art intent learning algorithms suffer from different drawbacks when only using a single type of data source. For example, query text has difficulty in distinguishing ambiguous queries; search log is bias to the order of search results and users' noisy click behaviors. In this paper, the authors for the first time leverage three types of objects, namely queries, web pages and Wikipedia concepts collaboratively for learning generic search intents and construct a heterogeneous graph to represent multiple types of relationships between them.
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