Abstract
The personalized recommendation model explores potential interests by analyzing consumers' historical behavior. Consumer psychology is an internal factor of consumer behavior; therefore, it is crucial to take into account its effect when improving the accuracy of recommendation. This paper proposes personalized commodity recommendation model based on consumer psychological effects. The quantitative approach rests on the fuzzy c‐means convolutional neural network (FCM‐CNN‐CPE). First, to alleviate data sparsity, we integrate the geographical location information into a fuzzy c‐means clustering algorithm to cluster consumers. Second, combining with the triple psychological effects of consumption (conformity effect, third‐person effect and marginal diminishing effect), the improved rating index and consumption probability measurement adjust the original rating information. Building on these, we propose a CNN‐based personalized commodity recommendation process and subsequently validate the proposed method's effectiveness through empirical analysis. The research contributes to the theoretical development of recommendation models integrating consumer psychological effects and offer guidance for personalized commodity recommendation in practical applications.
Wu, L.; Su, W.; Zhang, C.; Baležentis, T. 2025. Consumer Psychological Effect Integrated Personalized Commodity Recommendation Model Based on the Fuzzy C‐Means Convolutional Neural Network. Psychology & marketing : Wiley. ISSN 0742-6046. eISSN 1520-6793. p. 1–25. DOI: 10.1002/mar.70053. [Scopus; Social Sciences Citation Index (Web of Science)].