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

• URL: http://arxiv.org/abs/2604.26983v1
• Date: Tue, 28 Apr 2026 18:16:30 GMT
• Title: Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
• Abstract summary: The proposed framework encodes revenue contributions in the user-item matrix and computes customer similarity directly on this basis.<n>We propose three recommendation strategies based on revenue share, product popularity, and expected profit generation.<n>The effectiveness of the proposed method is validated through simulation experiments and a real-world application using the UCI Online Retail dataset.
• Score: 1.2222488876888666
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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