MACHINE-LEARNING–BASED PRODUCT ASSORTMENT RECOMMENDATION FOR RETAIL OUTLETS IN DISTRIBUTION: CROSS-SELL AND ASSORTMENT-WIDTH GROWTH (CONCEPTUAL MODEL)

Ne’matov Abdug‘ani; Ismailov Shixnazar; Ashiraliyev Zokirjon

Authors

Ne’matov Abdug‘ani Candidate of Physical and Mathematical Sciences, Docent, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Multimedia Technologies
Ismailov Shixnazar Docent, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi, Department of Multimedia Technologies
Ashiraliyev Zokirjon Master’s student, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi

Keywords:

product recommendation, market-basket, item-item similarity, cross-sell, assortment width, MSL, FMCG.

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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