Product consumptions meet reviews: Inferring consumer preferences by an explainable machine learning approach

Abstract

Inferring consumers' preferences provides a better understanding of their purchase behavior, which is very important for business success, e.g., recommendation systems and targeted advertising. In this paper, we propose an explainable machine learning approach, namely Multi-view Latent Dirichlet Allocation (MVLDA), to infer and interpret consumer preferences. In the proposed model, we assume that there exists a downstream relationship between consumers' motivations and their purchase behaviors. We model this downstream relationship by linking two types of topics (i.e., textual topics for motivations and product-related topics for purchase behaviors), to quantify and explain consumers' choices. We validate our modeling framework using a real-world dataset collected from the online retailer Amazon. The experimental results show that the proposed model identifies a set of high-quality textual topics but also interprets its effect on consumer choices based on product-related topics. In addition, we demonstrate that the proposed model quantifies the consumer's preferences. The proposed model yields interesting insights about user preferences, and provides several important managerial implications, e.g., e-commerce platforms, brand managers, and marketers.