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A framework for itemset placement with diversification for retail businesses

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Abstract

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.

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Authors and Affiliations

  1. Ashoka University, Sonipat, India

    Anirban Mondal & Raghav Mittal

  2. Shiv Nadar University, Greater Noida, India

    Parul Chaudhary

  3. International Institute of Information Technology, Hyderabad, India

    Polepalli Krishna Reddy

Authors
  1. Anirban Mondal
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  2. Raghav Mittal
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  3. Parul Chaudhary
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  4. Polepalli Krishna Reddy
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Correspondence to Anirban Mondal.

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Mondal, A., Mittal, R., Chaudhary, P. et al. A framework for itemset placement with diversification for retail businesses. Appl Intell 52, 14541–14559 (2022). https://doi.org/10.1007/s10489-022-03250-8

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