Estimating product-choice probabilities from recency and frequency of page views
Estimating product-choice probabilities from recency and frequency of page views
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DOI：
10.1016/j.knosys.2016.02.006
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发表时间：
2016-05
期刊：
影响因子：
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通讯作者：
J. Iwanaga;Naoki Nishimura;Noriyoshi Sukegawa;Yuichi Takano
中科院分区：
文献类型：
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作者：
J. Iwanaga;Naoki Nishimura;Noriyoshi Sukegawa;Yuichi Takano
本文研究了消费者的页面浏览量与其在电子商务网站上选择产品的概率之间的关系。为此，我们创建了一个概率表，其中包含每个客户以前的VP的所有最近和频率组合的产品选择概率。在训练样本较少的情况下，为了减少估计误差，我们建立了关于新近和频率满足单调性、凸性和凹性约束的乘积选择概率的优化模型。计算结果表明，该方法比Logistic回归和基于核的支持向量机具有明显的优势。
This paper investigates the relationship between customers’ page views (PVs) and the probabilities of their product choices on e-commerce sites. For this purpose, we create a probability table consisting of product-choice probabilities for all recency and frequency combinations of each customers’ previous PVs. To reduce the estimation error when there are few training samples, we develop optimization models for estimating the product-choice probabilities that satisfy monotonicity, convexity and concavity constraints with respect to recency and frequency. Computational results demonstrate that our method has clear advantages over logistic regression and kernel-based support vector machine.