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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.15229 |
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| _version_ | 1866912909372686336 |
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| author | Mazzetto, Alessio Khalili, Mohammad Mahdi Nern, Laura Fee Viderman, Michael Shtoff, Alex Dembczyński, Krzysztof |
| author_facet | Mazzetto, Alessio Khalili, Mohammad Mahdi Nern, Laura Fee Viderman, Michael Shtoff, Alex Dembczyński, Krzysztof |
| contents | We address prediction problems on tabular categorical data, where each instance is defined by multiple categorical attributes, each taking values from a finite set. These attributes are often referred to as fields, and their categorical values as features. Such problems frequently arise in practical applications, including click-through rate prediction and social sciences. We introduce and analyze {tensorFM}, a new model that efficiently captures high-order interactions between attributes via a low-rank tensor approximation representing the strength of these interactions. Our model generalizes field-weighted factorization machines. Empirically, tensorFM demonstrates competitive performance with state-of-the-art methods. Additionally, its low latency makes it well-suited for time-sensitive applications, such as online advertising. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_15229 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions Mazzetto, Alessio Khalili, Mohammad Mahdi Nern, Laura Fee Viderman, Michael Shtoff, Alex Dembczyński, Krzysztof Machine Learning Information Retrieval 68T05, 15A69 I.2.6 We address prediction problems on tabular categorical data, where each instance is defined by multiple categorical attributes, each taking values from a finite set. These attributes are often referred to as fields, and their categorical values as features. Such problems frequently arise in practical applications, including click-through rate prediction and social sciences. We introduce and analyze {tensorFM}, a new model that efficiently captures high-order interactions between attributes via a low-rank tensor approximation representing the strength of these interactions. Our model generalizes field-weighted factorization machines. Empirically, tensorFM demonstrates competitive performance with state-of-the-art methods. Additionally, its low latency makes it well-suited for time-sensitive applications, such as online advertising. |
| title | tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions |
| topic | Machine Learning Information Retrieval 68T05, 15A69 I.2.6 |
| url | https://arxiv.org/abs/2602.15229 |