Comparing Simple Association-Rules and Repeat-Buying Based Recommender Systems in a B2B Environment

In this contribution we present a systematic evaluation and comparison of recommender systems based on simple association rules and on repeat-buying theory. Both recommender services are based on the customer purchase histories of a medium-sized B2B-merchant for computer accessories. With the help of product managers an evaluation set for recommendations was generated. With regard to this evaluation set , recommendations produced by both methods are evaluated and several error measures are computed. This provides an empirical test whether frequent item sets or outliers of a stochastic purchase incidence model are suitable concepts for automatically generating recommendations. Furthermore, the loss functions (performance measures) of the two methods are compared and the sensitivity with regard to a misspecification of the model parameters is discussed.