You Say Factorization Machine, I Say Neural Network-It's All in the Activation

Chen Almagor, Yedid Hoshen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In recent years, many methods for machine learning on tabular data were introduced that use either factorization machines, neural networks or both. This created a great variety of methods making it non-obvious which method should be used in practice. We begin by extending the previously established theoretical connection between polynomial neural networks and factorization machines (FM) to recently introduced FM techniques. This allows us to propose a single neural-network-based framework that can switch between the deep learning and FM paradigms by a simple change of an activation function. We further show that an activation function exists which can adaptively learn to select the optimal paradigm. Another key element in our framework is its ability to learn high-dimensional embeddings by low-rank factorization. Our framework can handle numeric and categorical data as well as multiclass outputs. Extensive empirical experiments verify our analytical claims. Source code is available at https://github.com/ChenAlmagor/FiFa

Original languageAmerican English
Title of host publicationRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery, Inc
Pages389-398
Number of pages10
ISBN (Electronic)9781450392785
DOIs
StatePublished - 12 Sep 2022
Event16th ACM Conference on Recommender Systems, RecSys 2022 - Seattle, United States
Duration: 18 Sep 202223 Sep 2022

Publication series

NameRecSys 2022 - Proceedings of the 16th ACM Conference on Recommender Systems

Conference

Conference16th ACM Conference on Recommender Systems, RecSys 2022
Country/TerritoryUnited States
CitySeattle
Period18/09/2223/09/22

Bibliographical note

Publisher Copyright:
© 2022 ACM.

Keywords

  • CTR prediction
  • activation
  • factorization machines
  • machine learning
  • neural networks
  • tabular data

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