计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 190-194.doi: 10.11896/j.issn.1002-137X.2019.09.027
葛绍林1,3, 叶剑2,3, 何明祥1
GE Shao-lin1,3, YE Jian2,3, HE Ming-xiang1
摘要: 近年来,网络零售保持高速增长,网站中富含大量的用户行为数据。电商平台中的用户对商品的操作行为可以体现用户偏好,如何利用用户行为挖掘用户偏好已经成为学术界和工业界的关注焦点,并已经取得了众多研究成果。然而,目前用户操作行为预测方法研究通常只针对用户某一类操作行为进行分析,无法完备反映用户行为的整体特征。因此,提出一种基于深度森林的用户购买行为预测模型,通过构建用户行为特征工程建立整体用户行为特征模型;基于此,提出基于深度森林的用户购买行为预测方法,实现高效的行为预测训练效果。该方法的训练时间为43s,F1值为9.73%,相对其他模型取得了更好的效果。实验结果表明,该模型在降低时间开销的同时,提高了预测准确率。
中图分类号:
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