Abstract:

We propose a method for induction of compact optimal recommendation policies based on discovery of frequent itemsets in a purchase database, followed by the application of standard decision tree learning algorithms for the purposes of simplification and compaction of the recommendation policies. Experimental results suggest that the structure of such policies can be exploited to partition the space of customer purchasing histories much more efficiently than frequent itemset discovery algorithms alone would allow.