用低秩张量捕捉类别特征高阶交互，提升预测效率
tensorFM: Low-Rank Approximations of Cross-Order Feature Interactions
- 通过低秩张量表示特征间高阶交互强度
- 性能媲美顶尖方法，推理延迟低
- 适合在线广告等实时场景
我们研究表格型类别数据的预测问题，每个实例由多个类别属性（字段）构成，每个属性取值来自有限集合。这类问题广泛存在于点击率预测和社会科学等领域。本文提出并分析了tensorFM，一种通过低秩张量逼近来高效捕捉属性间高阶交互的新模型。该模型是场加权因子分解机的推广。实验表明，tensorFM在性能上与当前先进方法相当，且具有低延迟特性，非常适合在线广告等时间敏感型应用。
原文摘要 · Abstract (English)
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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