Comparing Simple Association-Rules and Repeat-Buying Based Recommender Systems in a B2B Environment
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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.
KeywordsAssociation Rule Recommender System Mining Association Rule Recommender Service Market Basket
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