Industrial Recommender System Principles, Technologies and Enterprise Applications

Recommender systems, as a highly popular AI technology in recent years, have been widely applied across various industries. They have transformed the way we interact with technology, influencing our choices and shaping our experiences. This book provides a comprehensive introduction to industrial recommender systems, starting with the overview of the technical framework, gradually delving into each core module such as content understanding, user profiling, recall, ranking, re-ranking and so on, and introducing the key technologies and practices in enterprises.

The book also addresses common challenges in recommendation cold start, recommendation bias and debiasing. Additionally, it introduces advanced technologies in the field, such as reinforcement learning, causal inference.

Professionals working in the fields of recommender systems, computational advertising, and search will find this book valuable. It is also suitable for undergraduate, graduate, and doctoral students majoring in artificial intelligence, computer science, software engineering, and related disciplines. Furthermore, it caters to readers with an interest in recommender systems, providing them with an understanding of the foundational framework, insights into core technologies, and advancements in industrial recommender systems.

The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.

Chapter 1 Introduction to Recommender Systems.- Chapter 2 Content Understanding.- Chapter 3 User Profiles.- Chapter 4 All-encompassing Recall.- Chapter 5 Personalized Ranking.- Chapter 6 Re-consider and Re-rank.- Chapter 7 Cold-start Recommendation.- Chapter 8 Magic Hands in Recommender System.- Chapter 9 AB Testing Platform: A Powerful Tool for System Evolution.- Chapter 10 Advanced Technologies in Recommender System.

Lantao Hu, graduated from Tsinghua University, Department of Computer Science. He is currently Technical Director of Recommendation Algorithm at Kuaishou, and previously worked as a Senior Algorithm Engineer at ByteDance and a Senior Researcher at Tencent. He has extensive practical experience in the field of recommender systems and has been in charge of several large-scale industrial recommender systems, including TikTok, Kuaishou, and WeChat's "Discover". His main research focus in recommender systems and has published six academic papers in relevant fields and holds five patents.

Yueting Li, graduated from Dalian University of Technology with a major in Computer Science. She previously worked at Baidu participated in the development of advertising CTR prediction model and was in charge of multiple recommender systems at Xiaomi, including Music, Reading, App Store, and Game Center etc. She has extensive practical experience in the field of recommendation and advertising andhas been involved in building several recommender systems from scratch. Currently, she has transitioned to the field of smart homes, exploring the application of AI technologies such as intelligent perception and intelligent recommendation in new scenarios.

Guangfan Cui, graduated from the Institution of Software, Chinese Academy of Sciences, is an Assistant Researcher at iQiyi, responsible for the short video recommendation business. He once worked as a recommendation algorithm engineer at Xiaomi, responsible for the recommendation tasks in the App Store, Game Center, and Youpin business lines, and built the deep recommendation engine for Xiaomi's vertical domain business from scratch. His main research interests include recommendation system, computational advertising, and search system, and he has published several papers and patents.

Kexin Yi, graduated from Peking University, worked as an algorithm engineer at iQiyi and Kuaishou, mainly focusing on causal inference, interest decoupling, sequence recommendation, and sample optimization.