Hu, Lantao.
Industrial Recommender System Principles, Technologies and Enterprise Applications / [electronic resource] : by Lantao Hu, Yueting Li, Guangfan Cui, Kexin Yi. - 1st ed. 2024. - XV, 246 p. 184 illus., 138 illus. in color. online resource.
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.
9789819725816
10.1007/978-981-97-2581-6 doi
Artificial intelligence.
Machine learning.
Expert systems (Computer science).
Artificial intelligence--Data processing.
Data mining.
Artificial Intelligence.
Machine Learning.
Knowledge Based Systems.
Data Science.
Data Mining and Knowledge Discovery.
Q334-342 TA347.A78
006.3
Industrial Recommender System Principles, Technologies and Enterprise Applications / [electronic resource] : by Lantao Hu, Yueting Li, Guangfan Cui, Kexin Yi. - 1st ed. 2024. - XV, 246 p. 184 illus., 138 illus. in color. online resource.
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.
9789819725816
10.1007/978-981-97-2581-6 doi
Artificial intelligence.
Machine learning.
Expert systems (Computer science).
Artificial intelligence--Data processing.
Data mining.
Artificial Intelligence.
Machine Learning.
Knowledge Based Systems.
Data Science.
Data Mining and Knowledge Discovery.
Q334-342 TA347.A78
006.3