Toward Memory-Efficient and Interpretable Factorization Machines via Data and Model Binarization
Factorization Machines (FM) is a general predictor that can efficiently model feature interactions in linear time, and thus has been broadly used for regression, classification and ranking tasks. Subspace Encoding Factorization Machine (SEFM) is one of the recent approaches which is proposed to enha...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10310141/ |