MACHINE-LEARNING–BASED PRODUCT ASSORTMENT RECOMMENDATION FOR RETAIL OUTLETS IN DISTRIBUTION: CROSS-SELL AND ASSORTMENT-WIDTH GROWTH (CONCEPTUAL MODEL)
Abdugani NematovCandidate of Physical and Mathematical Sciences, Docent, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Multimedia TechnologiesShixnazar IsmailovDocent, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Multimedia TechnologiesZokirjon AshiraliyevMaster's student, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
Zenodo (CERN European Organization for Nuclear Research)repository2026
ABI
Abstract
When an agent or van-seller visits an outlet, an important decision is to offer a product that the outlet does not yet sell but is likely to accept (cross-sell), thereby increasing assortment width and the average order. The concluding part of this three-part series proposes a conceptual model for recommending products to each outlet based on item-item similarity (market-basket) and describes the methodology for comparison with a popularity-based baseline (Precision@k, Recall@k).
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