An Input-aware Factorization Machine for Sparse Prediction

An Input-aware Factorization Machine for Sparse Prediction

Yantao Yu, Zhen Wang, Bo Yuan

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 1466-1472. https://doi.org/10.24963/ijcai.2019/203

Factorization machines (FMs) are a class of general predictors working effectively with sparse data, which represents 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 individual input upon the representation of features. We propose a novel model named \textit{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 state-of-the-art deep learning based methods.
Keywords:
Humans and AI: Personalization and User Modeling
Machine Learning: Data Mining
Machine Learning: Recommender Systems
Multidisciplinary Topics and Applications: Information Retrieval