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 learn personalized query auto-completion models. We propose a new Transformer-based hierarchical encoder to model different kinds of sequential behaviors, which can be seen as multiple distinct views of the user’s searching history, and then a prefix-to-history attention mechanism is used to select the most relevant information to compose the final intention representation. To learn more informative representations, we propose to incorporate multi-task learning into the model training. Two different kinds of supervisory information provided by query logs are utilized at the same time by jointly training a CTR prediction model and a query generation model.

To bridge the gap between the setting of research work and the real scenario, we release a new large-scale query log dataset - TaobaoQAC, which contains rich real prefix-to-query click behaviors. We conduct experiments on TaobaoQAC to demonstrate the effectiveness or our approach, and results show that M2A achieves superior performance compared with several strong baselines in both candidate ranking and query generation. We also conduct an online A/B testing and our approach has been deployed online.