Higher-Order Factorization Machines

2016/07/25 by Mathieu Blondel, Blondel, Mathieu, Akinori Fujino +5 · 18 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Bioinformatics #Text and Document Classification Technologies

paper · pdf · doi:10.48550/arxiv.1607.07195

openalex publication_date 2016/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• BPR: Bayesian Personalized Ranking from Implicit Feedback

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