Dynamic Bayesian Network–Based Product Recommendation Considering Consumers’ Multistage Shopping Journeys: A Marketing Funnel Perspective

Qiang Wei (), Yao Mu (), Xunhua Guo (), Weijie Jiang () and Guoqing Chen ()
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Qiang Wei: School of Economics and Management, Tsinghua University, Beijing 100084, China
Yao Mu: Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai 201620, China
Xunhua Guo: China Retail Research Center, School of Economics and Management, Tsinghua University, Beijing 100084, China
Weijie Jiang: School of Economics and Management, Tsinghua University, Beijing 100084, China
Guoqing Chen: School of Economics and Management, Tsinghua University, Beijing 100084, China

Information Systems Research, 2024, vol. 35, issue 3, 1382-1402

Abstract: Recommender systems are widely used by online merchants to find the products that are likely to interest consumers, but existing dynamic methods still face challenges regarding diverse behaviors, variability in interest shifts, and the identification of psychological dynamics. Premised on the marketing funnel perspective to analyze consumer shopping journeys, this study proposes a novel machine learning approach for product recommendation, namely, multistage dynamic Bayesian network (MS-DBN), to model the generative processes of consumers’ interactive behaviors with products in light of stage transitions and interest shifts. This approach features a dynamic Bayesian network model to overcome the problem of diverse behaviors and extract generalizable regularity of consumers’ psychological dynamics, two latent layers to depict variability in consumers’ interest shifts across multiple stages, and the identification strategies that dynamically detect the invisible stages and interests of consumers. Extensive experiments on large-scale real-world data and comprehensive robustness checks manifest the superior performance of the proposed MS-DBN approach over baseline methods.

Keywords: product recommendation; multistage shopping journey; dynamic Bayesian network; stage transition; interest shift; marketing funnel (search for similar items in EconPapers)
Date: 2024
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