Personalized Promotion Recommendation Through Consumer Experience Evolution Modeling

Recent years have witnessed the great passion of shoppers to purchase products at promotion, resulting in "smarter" consumers with growing price sensitivity towards promotion. In order to provide such price sensitive consumers with personalized promotion recommendations, it is important to take account of the temporal uncertainty of consumer preference as well as price sensitivity simultaneously. Although consumer preference has been richly studied in recommender system, little attention has been paid to exploring the uncertainty in consumers' growing price sensitivity. In this regard, this paper seeks to bridge the gap by modeling the temporal dynamics of consumer preference and price sensitivity in a combined manner through the lens of consumer experience evolution. Given the commonly implicit nature of consumer behavior, a pairwise learning framework built on feature-based latent factor model and enhanced Bayesian personalized ranking (exFBPR) is proposed along with a corresponding learning algorithm tailored for experience evolution and multiple feedbacks is developed accordingly. Furthermore, extensive empirical experiments a real-world dataset show the superiority of the proposed framework.