# Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure

arXiv: https://arxiv.org/abs/2604.26983 · HTML: https://mlsanity.com/paper/2604.26983/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 28 Apr 2026.

## Builds on (most cited first, 12 of 12)

- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [Data Scarcity in Recommendation Systems: A Survey](https://mlsanity.com/paper/2312.10073/) (2023)
- [Interpreting the Curse of Dimensionality from Distance Concentration and Manifold Effect](https://mlsanity.com/paper/2401.00422/) (2023)
- [On perfect clustering of high dimension, low sample size data](https://mlsanity.com/paper/1612.09121/) (2016)
- [Trust your neighbors: A comprehensive survey of neighborhood-based methods for recommender systems](https://mlsanity.com/paper/2109.04584/) (2021)
- [Price and Profit Awareness in Recommender Systems](https://mlsanity.com/paper/1707.08029/) (2017)
- [Contemporary Recommendation Systems on Big Data and Their Applications: A Survey](https://mlsanity.com/paper/2206.02631/) (2022)
- [Recommender Systems for Sustainability: Overview and Research Issues](https://mlsanity.com/paper/2412.03620/) (2024)
- [Show Me the Money: Dynamic Recommendations for Revenue Maximization](https://mlsanity.com/paper/1409.0080/) (2014)
- [Review of Clustering-Based Recommender Systems](https://mlsanity.com/paper/2109.12839/) (2021)
- [Exploring Customer Price Preference and Product Profit Role in Recommender Systems](https://mlsanity.com/paper/2203.06641/) (2022)
- [Economic Recommender Systems -- A Systematic Review](https://mlsanity.com/paper/2308.11998/) (2023)

## Abstract (arXiv)

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.