Abstract
Factorization machines (FMs) are a class of general predictors working effectively with sparse data,
which represent features using factorized parameters and weights. However, the accuracy of FMs
can be adversely affected by the fixed representation trained for each feature, as the same feature is
usually not equally predictive and useful in different instances. In fact, the inaccurate representation
of features may even introduce noise and degrade
the overall performance. In this work, we improve
FMs by explicitly considering the impact of each
individual input upon the representation of features.
We propose a novel model named Input-aware Factorization Machine (IFM), which learns a unique
input-aware factor for the same feature in different instances via a neural network. Comprehensive
experiments on three real-world recommendation
datasets are used to demonstrate the effectiveness
and mechanism of IFM. Empirical results indicate
that IFM is significantly better than the standard
FM model and consistently outperforms four stateof-the-art deep learning based methods