Query auto completion (QAC) systems are a standard part of search engines in
industry, helping users formulate their query. Such systems update their
suggestions after the user types each character, predicting the user's intent
using various signals - one of the most common being popularity. Recently, deep
learning approaches have been proposed for the QAC task, to specifically
address the main limitation of previous popularity-based methods: the inability
to predict unseen queries. In this work we improve previous methods based on
neural language modeling, with the goal of building an end-to-end system. We
particularly focus on using real-world data by integrating user information for
personalized suggestions when possible. We also make use of time information
and study how to increase diversity in the suggestions while studying the
impact on scalability. Our empirical results demonstrate a marked improvement
on two separate datasets over previous best methods in both accuracy and
scalability, making a step towards neural query auto-completion in production
search engines.

本研究提出了基于神经语言建模的深度学习方法，以改善基于流行度的方法无法预测未见过的查询的主要限制，并通过集成用户信息实现个性化建议，使用时间信息并研究如何增加建议的多样性，从而在准确性和可扩展性方面显著改善前人最佳方法，迈向生产搜索引擎中的神经查询自动完成。