基于深度学习的查询建议综述  

Review of Deep Learning Based Query Suggestion

作　　者：田萱[1,2] 徐泽洲 王子涵 Tian Xuan;Xu Zezhou;Wang Zihan(School of Information Science and Technology,Beijing Forestry University,Beijing 100083;National Forestry Grassland Forestry Intelligent Information Processing Engineering Technology Research Center(Beijing Forestry University),Beijing 100083)

机构地区：[1]北京林业大学信息学院,北京100083 [2]国家林业草原林业智能信息处理工程技术研究中心(北京林业大学),北京100083

出　　处：《计算机研究与发展》2024年第12期3168-3187,共20页Journal of Computer Research and Development

摘　　要：查询建议是当今搜索引擎必不可少的一个组成部分,它可以在用户输入完整查询前提供查询候选项,帮助用户更准确、更快速地表达信息需求.深度学习技术有助于提升查询建议的准确度,成为近年来推动查询建议发展的主流技术.主要对基于深度学习的查询建议研究现状进行归纳整理与分析对比,根据深度学习应用阶段不同,把其分为生成式查询建议与排名式查询建议2类,分析其中每种模型的建模思路和处理特征.此外还介绍了查询建议领域常用的数据集、基线方法与评价指标,并对比其中不同模型的技术特点与实验结果.最后总结了基于深度学习的查询建议研究目前面临的挑战与未来发展趋势.Query suggestion(QS)is an indispensable part of search engines.It can provide query candidates before users entering a complete query to help express their information needs more accurately and more quickly.Deep learning helps to improve the accuracy of QS and it has become the mainstream technology to promote the development of QS in recent years.We mainly summarize,analyze and compare the research status of deep learning based QS(DQS).According to the different application stages of deep learning,DQS methods are divided into two categories:generative QS methods and ranking-based QS suggestion methods,and the modeling ideas of each model are analyzed.In addition,the data sets,baselines and evaluation indexes commonly used in the field of QS are introduced,and the technical characteristics and experimental results of different models are compared.Finally,the current challenges and future development trends of QS research based on deep learning are summarized.

关 键 词：查询建议 深度学习 查询自动补全 编码器-解码器 神经语言模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]