Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
Abstract
This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-item data while explicitly incorporating the contribution of each product and user to overall sales revenue. The proposed framework encodes revenue contributions in the user-item matrix and computes customer similarity directly on this basis using suitable distance measures. This enables the segmentation of users according to the revenue-based similarity of their purchase baskets and supports recommendations aligned with profitability objectives. We compare conventional similarity metrics with a novel alternative tailored to high-dimensional contexts and propose three recommendation strategies based on revenue share, product popularity, and expected profit generation. The effectiveness of the proposed method is validated through simulation experiments and a real-world application using the UCI Online Retail dataset.
Cite
@article{arxiv.2604.26983,
title = {Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure},
author = {María Florencia Acosta and Rodrigo García Arancibia and Pamela Llop and Mariel Lovatto and Lucas Mansilla},
journal= {arXiv preprint arXiv:2604.26983},
year = {2026}
}