Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866909001171599360 |
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| author | Acosta, María Florencia Arancibia, Rodrigo García Llop, Pamela Lovatto, Mariel Mansilla, Lucas |
| author_facet | Acosta, María Florencia Arancibia, Rodrigo García Llop, Pamela Lovatto, Mariel Mansilla, Lucas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_26983 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure Acosta, María Florencia Arancibia, Rodrigo García Llop, Pamela Lovatto, Mariel Mansilla, Lucas Information Retrieval Machine Learning 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. |
| title | Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure |
| topic | Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2604.26983 |