The hybrid Poisson aspect model for personalized shopping recommendation

Predicting an individual customer's likelihood of purchasing a specific item forms the basis of many marketing activities, such as personalized shopping recommendation. Collaborative filtering and association rule mining can be applied to this problem, but in retail supermarkets, the problem becomes particularly challenging because of the sparsity and skewness of transaction data. We present HyPAM (hybrid Poisson aspect model), a new probabilistic graphical model that combines a Poisson mixture with a latent aspect class model to model customers' shopping behavior. We empirically compare HyPAM with two well-known recommenders, GroupLens (a correlation-based method), and IBM SmartPad (association rules and cosine similarity). Experimental results show that HyPAM outperforms the other recommenders by a large margin for two real-world retail supermarkets, ranking most of actual purchases in the top ten percent of the most likely purchased items. We also present a new visualization method, rank plot, to evaluate the quality of recommendations.