國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Temporal modelling of customer behaviour

Luo, Ling.

Temporal modelling of customer behaviour

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Temporal modelling of customer behaviourby Ling Luo.
作者:	Luo, Ling.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xv, 123 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Consumer behaviorMathematical models.
電子資源:	https://doi.org/10.1007/978-3-030-18289-2
ISBN:	9783030182892$q(electronic bk.)

Temporal modelling of customer behaviour
Luo, Ling.

Temporal modelling of customer behaviour[electronic resource] /by Ling Luo. - Cham :Springer International Publishing :2020. - xv, 123 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

This book describes advanced machine learning models - such as temporal collaborative filtering, stochastic models and Bayesian nonparametrics - for analysing customer behaviour. It shows how they are used to track changes in customer behaviour, monitor the evolution of customer groups, and detect various factors, such as seasonal effects and preference drifts, that may influence customers' purchasing behaviour. In addition, the book presents four case studies conducted with data from a supermarket health program in which the customers were segmented and the impact of promotional activities on different segments was evaluated. The outcomes confirm that the models developed here can be used to effectively analyse dynamic behaviour and increase customer engagement. Importantly, the methods introduced here can also be used to analyse other types of behavioural data such as activities on social networks, and educational systems.

ISBN: 9783030182892$q(electronic bk.)

Standard No.: 10.1007/978-3-030-18289-2doiSubjects--Topical Terms:

190790
Consumer behavior
--Mathematical models.

LC Class. No.: HF5415.32

Dewey Class. No.: 658.8342

Temporal modelling of customer behaviour
LDR:01921nmm a2200325 a 4500 001 578137
003 DE-He213
005 20200210094039.0
006 m d
007 cr nn 008maaau
008 201208s2020 sz s 0 eng d
020 $a 9783030182892$q(electronic bk.)
020 $a 9783030182885$q(paper)
024 7 $a 10.1007/978-3-030-18289-2 $2 doi
035 $a 978-3-030-18289-2
040 $a GP $c GP
041 0 $a eng
050 4 $a HF5415.32
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 658.8342 $2 23
090 $a HF5415.32 $b .L964 2020
100 1 $a Luo, Ling. $3 866738
245 1 0 $a Temporal modelling of customer behaviour $h [electronic resource] / $c by Ling Luo.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xv, 123 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5053
520 $a This book describes advanced machine learning models - such as temporal collaborative filtering, stochastic models and Bayesian nonparametrics - for analysing customer behaviour. It shows how they are used to track changes in customer behaviour, monitor the evolution of customer groups, and detect various factors, such as seasonal effects and preference drifts, that may influence customers' purchasing behaviour. In addition, the book presents four case studies conducted with data from a supermarket health program in which the customers were segmented and the impact of promotional activities on different segments was evaluated. The outcomes confirm that the models developed here can be used to effectively analyse dynamic behaviour and increase customer engagement. Importantly, the methods introduced here can also be used to analyse other types of behavioural data such as activities on social networks, and educational systems.
650 0 $a Consumer behavior $x Mathematical models. $3 190790
650 0 $a Marketing $x Mathematical models. $3 246079
650 1 4 $a Computational Intelligence. $3 338479
650 2 4 $a Consumer Behavior. $3 772804
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Market Research/Competitive Intelligence. $3 731061
650 2 4 $a Health Promotion and Disease Prevention. $3 274213
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Springer theses. $3 557607
856 4 0 $u https://doi.org/10.1007/978-3-030-18289-2
950 $a Intelligent Technologies and Robotics (Springer-42732)