A Retail Itemset Placement Framework Based on Premiumness of Slots and Utility Mining - Anirban Mondal (IEEE, 2021)
Retailer revenue is significantly impacted by item placement in retail stores. Notably, placement of items in the premium slots (i.e., slots with increased visibility/accessibility) improves the probability of sale w.r.t. item placement in non-premium slots. Moreover, customers often tend to buy sets of items (i.e., itemsets) instead of individual purchases. In this paper, we address the problem of maximizing retailer revenue by determining the placement of itemsets in different types of slots with varied premiumness. Our key contributions are as follows. First, we introduce the notion of premiumness of retail slots and discuss the issue of itemset placement in slots with varied premiumness. Second, we propose two efficient schemes, namely ${P}$ remiumness and ${R}$ evenue-based ${I}$ temset ${P}$ lacement (PRIP) and ${P}$ remiumness and ${A}$ verage ${R}$ evenue-based ${I}$ temset ${P}$ lacement (PARIP), for placing itemsets with varying revenue in slots with varied premiumness. Third, we perform a detailed performance analysis using both real and synthetic datasets to showcase the effectiveness of our proposed schemes. We also perform a comprehensive mathematical analysis of our proposed schemes w.r.t. the complexity analysis.