大葉大學圖書館 |

Spatio-temporal recommendation in social media[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	005.56
書名/作者:	Spatio-temporal recommendation in social media/ by Hongzhi Yin, Bin Cui.
作者:	Yin, Hongzhi.
其他作者:	Cui, Bin.
出版者:	Singapore : : Springer Singapore :, 2016.
面頁冊數:	xiii, 114 p. : : ill. (some col.), digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Recommender systems (Information filtering)
標題:	Social media.
標題:	Computer Science.
標題:	Data Mining and Knowledge Discovery.
標題:	Information Storage and Retrieval.
標題:	Information Systems Applications (incl. Internet)
標題:	Database Management.
ISBN:	9789811007484
ISBN:	9789811007477
內容註:	1. Introduction -- 2. Temporal Context-Aware Recommendation -- 3. Spatial Context-Aware Recommendation -- 4. Location-based and Real-time Recommendation -- 5. Fast Online Recommendation.
摘要、提要註:	This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users' behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users' behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.
電子資源:	http://dx.doi.org/10.1007/978-981-10-0748-4

Spatio-temporal recommendation in social media[electronic resource] /
Yin, Hongzhi.

Spatio-temporal recommendation in social media[electronic resource] /by Hongzhi Yin, Bin Cui. - Singapore :Springer Singapore :2016. - xiii, 114 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..

1. Introduction -- 2. Temporal Context-Aware Recommendation -- 3. Spatial Context-Aware Recommendation -- 4. Location-based and Real-time Recommendation -- 5. Fast Online Recommendation.

This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users' behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users' behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.

ISBN: 9789811007484

Standard No.: 10.1007/978-981-10-0748-4doiSubjects--Topical Terms:

467318
Recommender systems (Information filtering)

LC Class. No.: QA76.9.I58

Dewey Class. No.: 005.56

Spatio-temporal recommendation in social media[electronic resource] /
LDR:02383nam a2200337 a 4500 001 450468
003 DE-He213
005 20161024151358.0
006 m d
007 cr nn 008maaau
008 161210s2016 si s 0 eng d
020 $a 9789811007484 $q (electronic bk.)
020 $a 9789811007477 $q (paper)
024 7 $a 10.1007/978-981-10-0748-4 $2 doi
035 $a 978-981-10-0748-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.I58
072 7 $a UNF $2 bicssc
072 7 $a UYQE $2 bicssc
072 7 $a COM021030 $2 bisacsh
082 0 4 $a 005.56 $2 23
090 $a QA76.9.I58 $b Y51 2016
100 1 $a Yin, Hongzhi. $3 646616
245 1 0 $a Spatio-temporal recommendation in social media $h [electronic resource] / $c by Hongzhi Yin, Bin Cui.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2016.
300 $a xiii, 114 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in computer science, $x 2191-5768
505 0 $a 1. Introduction -- 2. Temporal Context-Aware Recommendation -- 3. Spatial Context-Aware Recommendation -- 4. Location-based and Real-time Recommendation -- 5. Fast Online Recommendation.
520 $a This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users' behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users' behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.
650 0 $a Recommender systems (Information filtering) $3 467318
650 0 $a Social media. $3 350802
650 1 4 $a Computer Science. $3 423143
650 2 4 $a Data Mining and Knowledge Discovery. $3 464541
650 2 4 $a Information Storage and Retrieval. $3 464540
650 2 4 $a Information Systems Applications (incl. Internet) $3 463679
650 2 4 $a Database Management. $3 463966
700 1 $a Cui, Bin. $3 646617
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in computer science. $3 466946
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-0748-4
950 $a Computer Science (Springer-11645)