語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Spatio-temporal recommendation in so...
~
Cui, Bin.
Spatio-temporal recommendation in social media
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Spatio-temporal recommendation in social mediaby 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)
電子資源:
http://dx.doi.org/10.1007/978-981-10-0748-4
ISBN:
9789811007484$q(electronic bk.)
Spatio-temporal recommendation in social media
Yin, Hongzhi.
Spatio-temporal recommendation in social media
[electronic resource] /by Hongzhi Yin, Bin Cui. - Singapore :Springer Singapore :2016. - xiii, 114 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in computer science,2191-5768. - SpringerBriefs in computer science..
1. Introduction -- 2. Temporal Context-Aware Recommendation -- 3. Spatial Context-Aware Recommendation -- 4. Location-based and Real-time Recommendation -- 5. Fast Online Recommendation.
This book covers the major fundamentals of and the latest research on next-generation spatio-temporal recommendation systems in social media. It begins by describing the emerging characteristics of social media in the era of mobile internet, and explores the limitations to be found in current recommender techniques. The book subsequently presents a series of latent-class user models to simulate users' behaviors in decision-making processes, which effectively overcome the challenges arising from temporal dynamics of users' behaviors, user interest drift over geographical regions, data sparsity and cold start. Based on these well designed user models, the book develops effective multi-dimensional index structures such as Metric-Tree, and proposes efficient top-k retrieval algorithms to accelerate the process of online recommendation and support real-time recommendation. In addition, it offers methodologies and techniques for evaluating both the effectiveness and efficiency of spatio-temporal recommendation systems in social media. The book will appeal to a broad readership, from researchers and developers to undergraduate and graduate students.
ISBN: 9789811007484$q(electronic bk.)
Standard No.: 10.1007/978-981-10-0748-4doiSubjects--Topical Terms:
310886
Recommender systems (Information filtering)
LC Class. No.: QA76.9.I58
Dewey Class. No.: 005.56
Spatio-temporal recommendation in social media
LDR
:02383nmm a2200337 a 4500
001
489120
003
DE-He213
005
20161024151358.0
006
m d
007
cr nn 008maaau
008
161213s2016 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
747388
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
310886
650
0
$a
Social media.
$3
465899
650
1 4
$a
Computer Science.
$3
212513
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
275288
650
2 4
$a
Information Storage and Retrieval.
$3
274190
650
2 4
$a
Information Systems Applications (incl. Internet)
$3
530743
650
2 4
$a
Database Management.
$3
273994
700
1
$a
Cui, Bin.
$3
747389
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
SpringerBriefs in computer science.
$3
559641
856
4 0
$u
http://dx.doi.org/10.1007/978-981-10-0748-4
950
$a
Computer Science (Springer-11645)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000126631
電子館藏
1圖書
電子書
EB QA76.9.I58 Y51 2016
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://dx.doi.org/10.1007/978-981-10-0748-4
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入