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

TMLR Paper5640 Authors

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.

Submission Length: Long submission (more than 12 pages of main content)

Assigned Action Editor: ~Piyush_Rai1

Submission Number: 5640