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.

中文翻译：

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面对购买：通过嵌入结构化面部和行为特征预测消费者选择

预测消费者的购买行为对于电子商务中的有针对性的广告和促销活动至关重要。人脸是深入了解消费者个性和行为特征的宝贵信息来源。然而，消费者的面部在以往的研究中大多未被探索，现有的面部相关研究侧重于人格特征等高级特征，而忽略了从面部数据中学习的商业意义。我们建议根据消费者的面部特征和购买历史来预测他们的购买行为。我们设计了一个基于分层嵌入网络的半监督模型来提取消费者的高级特征并预测消费者的前 N$ 购买目的地。