计算机科学
维数(图论)
人工智能
集成学习
机器学习
集合预报
随机森林
特征(语言学)
同种类的
堆积
融合
特征工程
人工神经网络
深度学习
数学
语言学
哲学
物理
核磁共振
组合数学
纯数学
作者
Xing Xie,Haiyuan Chen,Jianjun Yu,Jiangtao Wang
出处
期刊:International Journal of Information Technologies and Systems Approach
[IGI Global]
日期:2022-11-16
卷期号:15 (1): 1-16
标识
DOI:10.4018/ijitsa.313972
摘要
Recently, fewer scholars consider the prediction of repeat purchases in new retail models. Based on the real data of community group buying enterprises, this paper will study the prediction of community group buying users' repurchase behavior. Firstly, this paper carries out feature engineering according to the characteristics of the community groups buying industry. Finally, 313 features are extracted from the user dimension, head dimension, and business personnel dimension, respectively. Then, based on the heterogeneous integrated learning method stacking, three two-tier fusion models with the same primary learners but different secondary learners are constructed. Two homogeneous ensemble learning models, random forest and lightgbm, and the traditional single machine learning model are introduced for comparative experiments. Experiments show that the fusion model based on ensemble learning method has better prediction performance than a single model. Among the fusion models, the stacking two-layer fusion model with neural network model as secondary learner is the best.
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