A framework for itemset placement with diversification for retail businesses

Alongside revenue maximization, retailers seek to offer a diverse range of items to facilitate sustainable revenue generation in the long run. Moreover, customers typically buy sets of items, i.e., itemsets , as opposed to individual items. Therefore, strategic placement of diversified and high-revenue itemsets is a priority for the retailer. Research efforts made towards the extraction and placement of high-revenue itemsets in retail stores do not consider the notion of diversification. Further, candidate itemsets generated using existing utility mining schemes usually explode; which can cause memory and retrieval-time issues. This work makes three key contributions. First, we propose an efficient framework for retrieval of high-revenue itemsets with a varying size and a varying degree of diversification. A higher degree of diversification is indicative of fewer repetitive items in the top-revenue itemsets. Second, we propose the kUI ( k U tility I temset) index for quick and efficient retrieval of diverse top- λ high-revenue itemsets. We also propose the HUDIP ( H igh- U tility and D iversified I temset P lacement) scheme, which exploits our proposed kUI index for placement of high-revenue and diversified itemsets. Third, our extensive performance study with both real and synthetic datasets demonstrates the effectiveness of our proposed HUDIP scheme in efficiently determining high-revenue and diversified itemsets.