The Hybrid Poisson Aspect Model for Personalized Shopping Recommendation

Authors:
Chun-Nan Hsu;Hao-Hsiang Chung;Han-Shen Huang
Affiliations:
-;-;-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

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Abstract

Predicting an individual customer's likelihood of purchasinga specific item forms the basis of many marketingactivities, such as personalized shopping recommendation.Collaborative filtering and association rule miningcan be applied to this problem, but in retail supermarkets,the problem becomes particularly challenging because ofthe sparsity and skewness of transaction data. This paperpresents HyPAM(Hybrid Poisson Aspect Model), a newprobabilistic graphical model that combines a Poisson mixturewith a latent aspect class model to model customers'shopping behavior. We empirically compare HyPAM withtwo well-known recommenders, GroupLens (a correlation-basedmethod), and IBM SmartPad (association rules andcosine similarity). Experimental results show that HyPAMoutperforms the other recommenders by a large margin fortwo real-world retail supermarkets, ranking most of actualpurchases in the top ten percent of the most likely purchaseditems. We also present a new visualization method, rankplot, to evaluate the quality of recommendations.