NeurIPS 2016poster270 citations
Higher-Order Factorization Machines
Mathieu Blondel, Akinori Fujino, Naonori Ueda, Masakazu Ishihata
Abstract
Factorization machines (FMs) are a supervised learning approach that can use second-order feature combinations even when the data is very high-dimensional. Unfortunately, despite increasing interest in FMs, there exists to date no efficient training algorithm for higher-order FMs (HOFMs). In this paper, we present the first generic yet efficient algorithms for training arbitrary-order HOFMs. We also present new variants of HOFMs with shared parameters, which greatly reduce model size and prediction times while maintaining similar accuracy. We demonstrate the proposed approaches on four different link prediction tasks.
BibTeX
@inproceedings{NIPS2016_158fc2dd,
author = {Blondel, Mathieu and Fujino, Akinori and Ueda, Naonori and Ishihata, Masakazu},
booktitle = {Advances in Neural Information Processing Systems},
editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Higher-Order Factorization Machines},
url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/158fc2ddd52ec2cf54d3c161f2dd6517-Paper.pdf},
volume = {29},
year = {2016}
}