论文标题
面对购买：用结构化面部和行为特征嵌入的消费者选择
Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding
论文作者
论文摘要
预测消费者的购买行为对于针对性的广告和电子商务促销至关重要。人的面孔是获得对消费者个性和行为特征的见解的宝贵信息来源。但是，在先前的研究中，消费者的面孔在很大程度上尚未探索，现有的与面部相关的研究集中在高级特征（例如人格特征）上，同时忽略了从面部数据中学习的业务意义。我们建议根据消费者的面部特征和采购历史来预测购买。我们设计了一个基于层次嵌入网络的半监督模型，以提取消费者的高级特征，并预测消费者的顶部购买目的地。我们对现实世界数据集的实验结果表明，将面部信息纳入预测消费者的购买行为的积极作用。
Predicting consumers' purchasing behaviors is critical for targeted advertisement and sales promotion in e-commerce. Human faces are an invaluable source of information for gaining insights into consumer personality and behavioral traits. However, consumer's faces are largely unexplored in previous research, and the existing face-related studies focus on high-level features such as personality traits while neglecting the business significance of learning from facial data. We propose to predict consumers' purchases based on their facial features and purchasing histories. We design a semi-supervised model based on a hierarchical embedding network to extract high-level features of consumers and to predict the top-$N$ purchase destinations of a consumer. Our experimental results on a real-world dataset demonstrate the positive effect of incorporating facial information in predicting consumers' purchasing behaviors.
