東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Recommender systems = frontiers and practices /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Recommender systems/ by Dongsheng Li ... [et al.].
其他題名:	frontiers and practices /
其他作者:	Li, Dongsheng.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xvi, 280 p. :ill., digital ;24 cm.
內容註:	Chapter 1. Overview of Recommender Systems -- Chapter 2. Classic Recommendation Algorithms -- Chapter 3. Foundations of Deep Learning -- Chapter 4. Deep Learning-based Recommendation Algorithms -- Chapter 5. Recommender System Frontier Topics. Chapter 6. Practical Recommender System -- Chapter 7. Summary and Outlook.
Contained By:	Springer Nature eBook
標題:	Recommender systems (Information filtering) -
電子資源:	https://doi.org/10.1007/978-981-99-8964-5
ISBN:	9789819989645

Recommender systems = frontiers and practices /
Recommender systemsfrontiers and practices /[electronic resource] :by Dongsheng Li ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xvi, 280 p. :ill., digital ;24 cm.

Chapter 1. Overview of Recommender Systems -- Chapter 2. Classic Recommendation Algorithms -- Chapter 3. Foundations of Deep Learning -- Chapter 4. Deep Learning-based Recommendation Algorithms -- Chapter 5. Recommender System Frontier Topics. Chapter 6. Practical Recommender System -- Chapter 7. Summary and Outlook.

This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.

ISBN: 9789819989645

Standard No.: 10.1007/978-981-99-8964-5doiSubjects--Topical Terms:

1002434
Recommender systems (Information filtering)

LC Class. No.: ZA3084

Dewey Class. No.: 005.56

Recommender systems = frontiers and practices /
LDR:02129nmm a2200349 a 4500 001 2369705
003 DE-He213
005 20240327063933.0
006 m d
007 cr nn 008maaau
008 241127s2024 si s 0 eng d
020 $a 9789819989645 $q (electronic bk.)
020 $a 9789819989638 $q (paper)
024 7 $a 10.1007/978-981-99-8964-5 $2 doi
035 $a 978-981-99-8964-5
040 $a GP $c GP
041 0 $a eng
050 4 $a ZA3084
072 7 $a UNH $2 bicssc
072 7 $a UND $2 bicssc
072 7 $a COM030000 $2 bisacsh
072 7 $a UNH $2 thema
072 7 $a UND $2 thema
082 0 4 $a 005.56 $2 23
090 $a ZA3084 $b .R311 2024
245 0 0 $a Recommender systems $h [electronic resource] : $b frontiers and practices / $c by Dongsheng Li ... [et al.].
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2024.
300 $a xvi, 280 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1. Overview of Recommender Systems -- Chapter 2. Classic Recommendation Algorithms -- Chapter 3. Foundations of Deep Learning -- Chapter 4. Deep Learning-based Recommendation Algorithms -- Chapter 5. Recommender System Frontier Topics. Chapter 6. Practical Recommender System -- Chapter 7. Summary and Outlook.
520 $a This book starts from the classic recommendation algorithms, introduces readers to the basic principles and main concepts of the traditional algorithms, and analyzes their advantages and limitations. Then, it addresses the fundamentals of deep learning, focusing on the deep-learning-based technology used, and analyzes problems arising in the theory and practice of recommender systems, helping readers gain a deeper understanding of the cutting-edge technology used in these systems. Lastly, it shares practical experience with Microsoft 's open source project Microsoft Recommenders. Readers can learn the design principles of recommendation algorithms using the source code provided in this book, allowing them to quickly build accurate and efficient recommender systems from scratch.
650 0 $a Recommender systems (Information filtering) $3 1002434
650 1 4 $a Information Storage and Retrieval. $3 761906
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Li, Dongsheng. $3 1915641
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-981-99-8964-5
950 $a Computer Science (SpringerNature-11645)