東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Industrial recommender system = principles, technologies and enterprise applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Industrial recommender system/ by Lantao Hu ... [et al.].
Reminder of title:	principles, technologies and enterprise applications /
other author:	Hu, Lantao.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xv, 246 p. :ill. (chiefly col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Recommender systems (Information filtering) -
Online resource:	https://doi.org/10.1007/978-981-97-2581-6
ISBN:	9789819725816

Industrial recommender system = principles, technologies and enterprise applications /
Industrial recommender systemprinciples, technologies and enterprise applications /[electronic resource] :by Lantao Hu ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xv, 246 p. :ill. (chiefly col.), digital ;24 cm.

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.

ISBN: 9789819725816

Standard No.: 10.1007/978-981-97-2581-6doiSubjects--Topical Terms:

1002434
Recommender systems (Information filtering)

LC Class. No.: ZA3084

Dewey Class. No.: 025.04

Industrial recommender system = principles, technologies and enterprise applications /
LDR:02904nmm a2200325 a 4500 001 2371272
003 DE-He213
005 20240601125440.0
006 m d
007 cr nn 008maaau
008 241127s2024 si s 0 eng d
020 $a 9789819725816 $q (electronic bk.)
020 $a 9789819725809 $q (paper)
024 7 $a 10.1007/978-981-97-2581-6 $2 doi
035 $a 978-981-97-2581-6
040 $a GP $c GP
041 1 $a eng $h chi
050 4 $a ZA3084
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 025.04 $2 23
090 $a ZA3084 $b .I42 2024
245 0 0 $a Industrial recommender system $h [electronic resource] : $b principles, technologies and enterprise applications / $c by Lantao Hu ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xv, 246 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Recommender systems (Information filtering) $3 1002434
650 0 $a Artificial intelligence $x Industrial applications. $3 653318
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Knowledge Based Systems. $3 3538738
650 2 4 $a Data Science. $3 3538937
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
700 1 $a Hu, Lantao. $3 3717158
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-97-2581-6
950 $a Computer Science (SpringerNature-11645)