Large-scale Ensemble Model for Customer Churn Prediction in Search Ads-Reference-Cited by-同舟云学术

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Large-scale Ensemble Model for Customer Churn Prediction in Search Ads

Published:2018-11-13 Issue:2 Volume:11 Page:262-270
ISSN:1866-9956
Container-title:Cognitive Computation
language:en
Short-container-title:Cogn Comput

Author:

Wang Qiu-Feng^ORCID,Xu Mirror,Hussain Amir

Funder

Jiangsu University Natural Science Programme Grant

Open Project Fund of the National Laboratory of Pattern Recognition

Key Program Special Fund in XJTLU

XJTLU Research Development Fund

UK Engineering and Physical Sciences Research Council (EPSRC) grant

Publisher

Springer Science and Business Media LLC

Subject

Cognitive Neuroscience,Computer Science Applications,Computer Vision and Pattern Recognition

Link

http://link.springer.com/article/10.1007/s12559-018-9608-3/fulltext.html

Reference56 articles.

1. Wang Q, Huang K, Li S, Yu W. Adaptive modeling for large-scale advertisers optimization. BMC Big Data Analytics 2017;2:8.

2. Kim HS, Yoon CH. Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Telecommun Policy 2004;28(9-10):751–65.

3. Hadden J, Tiwari A, Roy R, Ruta D. Computer assisted customer churn management: state-of-the-art and future trends. Comput Oper Res 2007;v34(10):2902–17.

4. Yoon S, Koehler J, Ghobarah A. 2010. Prediction of advertiser churn for google adwords jsm proceedings.

5. Vafeiadis T, Diamantaras KI, Sarigiannidis G, et al. A comparison of machine learning techniques for customer churn prediction. Simul Model Pract Theory 2015;55:1–9.

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