Learning to Generate Personalized Query Auto-Completions via a Multi-View Multi-Task Attentive Approach
KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Virtual Event CA USA July, 2020, pp. 2998-3007, 2020.
By utilizing the information from multiple views and multiple tasks, the click-through rate prediction model, termed as M2A(CP), and the query generation model, termed as M2A(QG), in our framework achieve the best performance in all ranking and generation models respectively
In this paper, we study the task of Query Auto-Completion (QAC), which is a very significant feature of modern search engines. In real industrial application, there always exist two major problems of QAC - weak personalization and unseen queries. To address these problems, we propose M2A, a multi-view multi-task attentive framework to lea...More
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