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A Hybrid Representation of Word Images for Keyword Spotting Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Order-Aware Embedding Non-sampling Factorization Machines for Context-Aware Recommendation

Authors : Qingzhi Hou, Yifeng Chen, Mei Yu, Ruiguo Yu, Jian Yu, Mankun Zhao, Tianyi Xu, Xuewei Li

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

FM can use the second-order feature interactions. Some researchers combine FM with deep learning to get the high-order interactions. However, these models rely on negative sampling. ENSFM adopts non-sampling and gets fine results, but it does not consider the high-order interactions. In this paper, we add the high-order interactions to ENSFM. We also introduce a technique called Order-aware Embedding. The excellent results show the effectiveness of our model.

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previous chapter MCRN: A New Content-Based Music Classification and Recommendation Network
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Literature
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Chen, C., Zhang, M., Ma, W., Liu, Y., Ma, S.: Efficient non-sampling factorization machines for optimal context-aware recommendation. In: WWW 2020
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Metadata
Title
Order-Aware Embedding Non-sampling Factorization Machines for Context-Aware Recommendation
Authors
Qingzhi Hou
Yifeng Chen
Mei Yu
Ruiguo Yu
Jian Yu
Mankun Zhao
Tianyi Xu
Xuewei Li
Copyright Year
2020
Book
Neural Information Processing

Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63820-7_89

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