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.

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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

Abstract:

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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