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

AI 总结

• We address prediction problems on tabular categorical data, where each instance is defined by multiple categorical attributes, each taking …
• 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…
• Our model generalizes field-weighted factorization machines
• Empirically, tensorFM demonstrates competitive performance with state-of-the-art methods

内容摘录

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.