BAYESIAN ANALYSIS OF CROSS-CATEGORY ATTRIBUTE PREFERENCES: PERSONALIZED PRODUCT RECOMMENDATIONS
Journal
品質學報
Journal Volume
24
Journal Issue
5
Pages
360-371
Date Issued
2017
Author(s)
Abstract
When choosing which product to recommend to a target customer, firms often rely upon content-based or collaborative filters that either do not account for the heterogeneity of their target market or do not consider the trade-offs that consumers are willing to make for different product options. In this research, we develop a framework for investigating individual consumer preferences. This framework incorporates two steps. First, the Bayesian Variable Selection method is employed in order to select important variables. Second, a Hierarchical Bayes Probit model is developed in order to reflect the heterogeneity of individual preferences. Our empirical results demonstrate that the proposed method performs well in terms of discovering individual preferences toward cross-category common attributes at the abstract level. These findings provide important insights for retailers currently looking for ways to differentiate themselves using personalized product recommendations.
Type
journal article