Deep Learning to Rank in Industrial Search Engines, Recommender Systems, and Online Advertising: An Overview and New Perspectives
Yulong Gu, Lixin Zou, Chenliang Li · ACM Transactions on Information Systems · 2026
Search engines, Recommender systems, and Online advertising are playing fundamental roles in modern web and mobile applications. In these information systems, the most significant component is the ranking system, which selects a list of items likely to interest a user from billions of candidate items. At its core, Deep learning to Rank (DLTR) has become indispensable for building high-performance ranking models, driving significant gains in user engagement and business growth. In this article, firstly, we outline the key problems and challenges in industrial-scale ranking systems. Secondly, we provide a comprehensive review of deep learning models deployed across multiple stages of the industrial ranking pipeline, including matching, pre-ranking, fine-grained ranking, post-ranking, and relevance-ranking. Finally, we explore novel perspectives for future research, such as leveraging Large Language Models (LLMs). The papers discussed in this survey are listed in https://github.com/guyulongcs/Awesome-Deep-Learning-Papers-for-Search-Recommendation-Advertising .