04263nam a22006135i 4500 978-981-97-2581-6 DE-He213 20251030111803.0 cr nn 008mamaa 240601s2024 si | s |||| 0|eng d 9789819725816 978-981-97-2581-6 10.1007/978-981-97-2581-6 doi Q334-342 TA347.A78 UYQ bicssc COM004000 bisacsh UYQ thema Hu, Lantao. author. aut http://id.loc.gov/vocabulary/relators/aut 74658 Industrial Recommender System [electronic resource] : Principles, Technologies and Enterprise Applications / by Lantao Hu, Yueting Li, Guangfan Cui, Kexin Yi. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XV, 246 p. 184 illus., 138 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. 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. Artificial intelligence. 58441 Machine learning. 55059 Expert systems (Computer science). 67822 Artificial intelligence Data processing. Data mining. 6941 Artificial Intelligence. 58441 Machine Learning. 55059 Knowledge Based Systems. 67823 Data Science. Data Mining and Knowledge Discovery. Li, Yueting. author. aut http://id.loc.gov/vocabulary/relators/aut 74659 Cui, Guangfan. author. aut http://id.loc.gov/vocabulary/relators/aut 74660 Yi, Kexin. author. aut http://id.loc.gov/vocabulary/relators/aut 74661 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819725809 Printed edition: 9789819725823 Printed edition: 9789819725830 https://doi.org/10.1007/978-981-97-2581-6 ZDB-2-SCS ZDB-2-SXCS 58920 58920