Higher-Order Factorization Machines

Understand

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.