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arXiv · 2602.15229

tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions

Alessio Mazzetto·Mohammad Mahdi Khalili·Laura Fee Nern·Michael Viderman·Alex Shtoff·Krzysztof Dembczyński
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Abstract

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.

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Alessio Mazzetto, Mohammad Mahdi Khalili, Laura Fee Nern, Michael Viderman, Alex Shtoff, Krzysztof Dembczyński. 2026-02-16. tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions. https://arxiv.org/abs/2602.15229

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