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

2026-04-28

AI Summary

This study addresses the challenge posed by the high-dimensional sparsity of user-item interaction data and its adverse impact on recommendation profitability. To this end, the authors propose a value-aware recommendation approach that explicitly encodes item profitability within the user-item matrix and introduces a profitability-aware similarity metric tailored for high-dimensional sparse settings. This enables user segmentation based on the profitability of their purchase baskets. Building upon this segmentation, three profit-oriented recommendation strategies—profit share, item popularity, and expected profit—are developed. Experimental evaluations on both synthetic data and the UCI Online Retail real-world dataset demonstrate that the proposed method significantly enhances the overall profitability of recommender systems.

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.

Problem

• value-aware recommendation
• high-dimensional data
• customer segmentation
• revenue contribution
• sparsity

Innovation

• value-aware recommendation
• high-dimensional similarity
• revenue-based user segmentation
• profit-oriented recommendation
• sparse user-item matrix