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Industrial Recommender System : Principles, Technologies and Enterprise Applications / by Lantao Hu, Yueting Li, Guangfan Cui, Kexin Yi

By: Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: Singapore : Springer Nature Singapore, 2024Publisher: Singapore : Imprint: Springer, 2024Edition: 1st ed. 2024Description: 1 Online-Ressource(XV, 246 p. 184 illus., 138 illus. in color.)ISBN:
  • 9789819725816
Subject(s): Additional physical formats: 9789819725809 | 9789819725823 | 9789819725830 | Erscheint auch als: 9789819725809 Druck-Ausgabe | Erscheint auch als: 9789819725823 Druck-Ausgabe | Erscheint auch als: 9789819725830 Druck-AusgabeDDC classification:
  • 006.3 23
DOI: DOI: 10.1007/978-981-97-2581-6Online resources: Summary: 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.Summary: 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.PPN: PPN: 1890573671Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-SCS | ZDB-2-SXCS
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