SE-stacking: Improving user purchase behavior prediction by information fusion and ensemble learning

Online shopping behavior has the characteristics of rich granularity dimension and data sparsity and presents a challenging task in e-commerce. Previous studies on user behavior prediction did not seriously discuss feature selection and ensemble design, which are important to improving the performan...

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Veröffentlicht in:	PloS one 2020-11, Vol.15 (11), p.e0242629-e0242629
Hauptverfasser:	Xu, Jing, Wang, Jie, Tian, Ye, Yan, Jiangpeng, Li, Xiu, Gao, Xin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Biobehavioral Sciences Biology and Life Sciences Computer and Information Sciences Consumer Behavior Electronic commerce Engineering and Technology Ensemble learning Humans Learning algorithms Machine Learning Online shopping Parameter modification Physical Sciences Predictions Purchasing Recommender systems Research and Analysis Methods Social Sciences Stacking User behavior
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