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2021 | OriginalPaper | Chapter

W2FM: The Doubly-Warped Factorization Machine

Authors : Mao-Lin Li, K. Selçuk Candan

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

The chapter 'W2FM: The Doubly-Warped Factorization Machine' introduces a novel approach to enhance Factorization Machines (FMs) by leveraging doubly-warped transformations. The authors address the limitations of conventional FMs in capturing complex non-linear structures by proposing a model that warps both the input space and the decision boundary. This innovative methodology aims to improve the representational ability and accuracy of FMs, particularly in sparse data settings. The chapter also presents various variants of the proposed model, each offering different trade-offs in terms of initialization, parameter sharing, and neural integration. Experimental results demonstrate the superior performance of the proposed model compared to existing methods, highlighting its potential to significantly advance the field of machine learning.
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Metadata
Title
W2FM: The Doubly-Warped Factorization Machine
Authors
Mao-Lin Li
K. Selçuk Candan
Copyright Year
2021
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-75764-9

Electronic ISBN: 978-3-030-75765-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-75765-6_39

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