IEICE TRANSACTIONS on Information
Purchase Behavior Prediction in E-Commerce with Factorization Machines
Summary :
Purchase behavior prediction is one of the most important issues for the precision marketing of e-commerce companies. This Letter presents our solution to the purchase behavior prediction problem in E-commerce, specifically the task of Big Data Contest of China Computer Federation in 2014. The goal of this task is to predict which users will have the purchase behavior based on users' historical data. The traditional methods of recommendation encounter two crucial problems in this scenario. First, this task just predicts which users will have the purchase behavior, rather than which items should be recommended to which users. Second, the large-scale dataset poses a big challenge for building the empirical model. Feature engineering and Factorization Model shed some light on these problems. We propose to use Factorization Machines model based on the multiple classes and high dimensions of feature engineering. Experimental results on a real-world dataset demonstrate the advantages of our proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E99-D No.1 pp.270-274
- Publication Date
- 2016/01/01
- Publicized
- 2015/10/01
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDL8116
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Chen CHEN
Nankai University
Chunyan HOU
Tianjin University of Technology
Jiakun XIAO
Nankai University
Xiaojie YUAN
Nankai University
Keyword
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Chen CHEN, Chunyan HOU, Jiakun XIAO, Xiaojie YUAN, "Purchase Behavior Prediction in E-Commerce with Factorization Machines" in IEICE TRANSACTIONS on Information,
vol. E99-D, no. 1, pp. 270-274, January 2016, doi: 10.1587/transinf.2015EDL8116.
Abstract: Purchase behavior prediction is one of the most important issues for the precision marketing of e-commerce companies. This Letter presents our solution to the purchase behavior prediction problem in E-commerce, specifically the task of Big Data Contest of China Computer Federation in 2014. The goal of this task is to predict which users will have the purchase behavior based on users' historical data. The traditional methods of recommendation encounter two crucial problems in this scenario. First, this task just predicts which users will have the purchase behavior, rather than which items should be recommended to which users. Second, the large-scale dataset poses a big challenge for building the empirical model. Feature engineering and Factorization Model shed some light on these problems. We propose to use Factorization Machines model based on the multiple classes and high dimensions of feature engineering. Experimental results on a real-world dataset demonstrate the advantages of our proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDL8116/_p
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@ARTICLE{e99-d_1_270,
author={Chen CHEN, Chunyan HOU, Jiakun XIAO, Xiaojie YUAN, },
journal={IEICE TRANSACTIONS on Information},
title={Purchase Behavior Prediction in E-Commerce with Factorization Machines},
year={2016},
volume={E99-D},
number={1},
pages={270-274},
abstract={Purchase behavior prediction is one of the most important issues for the precision marketing of e-commerce companies. This Letter presents our solution to the purchase behavior prediction problem in E-commerce, specifically the task of Big Data Contest of China Computer Federation in 2014. The goal of this task is to predict which users will have the purchase behavior based on users' historical data. The traditional methods of recommendation encounter two crucial problems in this scenario. First, this task just predicts which users will have the purchase behavior, rather than which items should be recommended to which users. Second, the large-scale dataset poses a big challenge for building the empirical model. Feature engineering and Factorization Model shed some light on these problems. We propose to use Factorization Machines model based on the multiple classes and high dimensions of feature engineering. Experimental results on a real-world dataset demonstrate the advantages of our proposed method.},
keywords={},
doi={10.1587/transinf.2015EDL8116},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Purchase Behavior Prediction in E-Commerce with Factorization Machines
T2 - IEICE TRANSACTIONS on Information
SP - 270
EP - 274
AU - Chen CHEN
AU - Chunyan HOU
AU - Jiakun XIAO
AU - Xiaojie YUAN
PY - 2016
DO - 10.1587/transinf.2015EDL8116
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2016
AB - Purchase behavior prediction is one of the most important issues for the precision marketing of e-commerce companies. This Letter presents our solution to the purchase behavior prediction problem in E-commerce, specifically the task of Big Data Contest of China Computer Federation in 2014. The goal of this task is to predict which users will have the purchase behavior based on users' historical data. The traditional methods of recommendation encounter two crucial problems in this scenario. First, this task just predicts which users will have the purchase behavior, rather than which items should be recommended to which users. Second, the large-scale dataset poses a big challenge for building the empirical model. Feature engineering and Factorization Model shed some light on these problems. We propose to use Factorization Machines model based on the multiple classes and high dimensions of feature engineering. Experimental results on a real-world dataset demonstrate the advantages of our proposed method.
ER -
English
