IEICE TRANSACTIONS on Information
Enhancing Purchase Behavior Prediction with Temporally Popular Items
Summary :
In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use traditional features based on the statistics and temporal dynamics of items. Those features lead to the loss of detailed items' information. In this study, we propose a novel kind of features based on temporally popular items to improve the prediction. Experiments on the real-world dataset have demonstrated the effectiveness and the efficiency of our proposed method. Features based on temporally popular items are compared with traditional features which are associated with statistics, temporal dynamics and collaborative filter of items. We find that temporally popular items are an effective and irreplaceable supplement of traditional features. Our study shed light on the effectiveness of the combination of popularity and temporal dynamics of items which can widely used for a variety of recommendations in e-commerce sites.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.9 pp.2237-2240
- Publication Date
- 2017/09/01
- Publicized
- 2017/05/30
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDL8057
- Type of Manuscript
- LETTER
- Category
- Artificial Intelligence, Data Mining
Authors
Chen CHEN
Nankai University
Chunyan HOU
Tianjin University of Technology
Jiakun XIAO
Nankai University
Yanlong WEN
Nankai University
Xiaojie YUAN
Nankai University
Keyword
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Chen CHEN, Chunyan HOU, Jiakun XIAO, Yanlong WEN, Xiaojie YUAN, "Enhancing Purchase Behavior Prediction with Temporally Popular Items" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 9, pp. 2237-2240, September 2017, doi: 10.1587/transinf.2017EDL8057.
Abstract: In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use traditional features based on the statistics and temporal dynamics of items. Those features lead to the loss of detailed items' information. In this study, we propose a novel kind of features based on temporally popular items to improve the prediction. Experiments on the real-world dataset have demonstrated the effectiveness and the efficiency of our proposed method. Features based on temporally popular items are compared with traditional features which are associated with statistics, temporal dynamics and collaborative filter of items. We find that temporally popular items are an effective and irreplaceable supplement of traditional features. Our study shed light on the effectiveness of the combination of popularity and temporal dynamics of items which can widely used for a variety of recommendations in e-commerce sites.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8057/_p
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@ARTICLE{e100-d_9_2237,
author={Chen CHEN, Chunyan HOU, Jiakun XIAO, Yanlong WEN, Xiaojie YUAN, },
journal={IEICE TRANSACTIONS on Information},
title={Enhancing Purchase Behavior Prediction with Temporally Popular Items},
year={2017},
volume={E100-D},
number={9},
pages={2237-2240},
abstract={In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use traditional features based on the statistics and temporal dynamics of items. Those features lead to the loss of detailed items' information. In this study, we propose a novel kind of features based on temporally popular items to improve the prediction. Experiments on the real-world dataset have demonstrated the effectiveness and the efficiency of our proposed method. Features based on temporally popular items are compared with traditional features which are associated with statistics, temporal dynamics and collaborative filter of items. We find that temporally popular items are an effective and irreplaceable supplement of traditional features. Our study shed light on the effectiveness of the combination of popularity and temporal dynamics of items which can widely used for a variety of recommendations in e-commerce sites.},
keywords={},
doi={10.1587/transinf.2017EDL8057},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Enhancing Purchase Behavior Prediction with Temporally Popular Items
T2 - IEICE TRANSACTIONS on Information
SP - 2237
EP - 2240
AU - Chen CHEN
AU - Chunyan HOU
AU - Jiakun XIAO
AU - Yanlong WEN
AU - Xiaojie YUAN
PY - 2017
DO - 10.1587/transinf.2017EDL8057
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2017
AB - In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use traditional features based on the statistics and temporal dynamics of items. Those features lead to the loss of detailed items' information. In this study, we propose a novel kind of features based on temporally popular items to improve the prediction. Experiments on the real-world dataset have demonstrated the effectiveness and the efficiency of our proposed method. Features based on temporally popular items are compared with traditional features which are associated with statistics, temporal dynamics and collaborative filter of items. We find that temporally popular items are an effective and irreplaceable supplement of traditional features. Our study shed light on the effectiveness of the combination of popularity and temporal dynamics of items which can widely used for a variety of recommendations in e-commerce sites.
ER -
English
