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Neural Hidden Markov Model

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Agents and Artificial Intelligence (ICAART 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11978))

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Abstract

Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation. It is applied for time-aware recommender systems to show the benefits from the hybrid of combining neural network and hidden Markov model. We implement the recommender system and experiment on real datasets to demonstrate better performances over the existing recommender systems.

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Acknowledgements

This work is supported by Natural Science Fund of China under numbers 61672049/61732001.

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Authors and Affiliations

  1. Department of Information Science, School of Mathematical Sciences, Peking University, Beijing, 100871, China

    Zuoquan Lin & Jiehu Song

Authors
  1. Zuoquan Lin
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  2. Jiehu Song
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Correspondence to Zuoquan Lin .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Jaap van den Herik

  2. LIACC, University of Porto, Porto, Portugal

    Ana Paula Rocha

  3. ICREA, Institute of Evolutionary Biology, Barcelona, Spain

    Luc Steels

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Lin, Z., Song, J. (2019). Neural Hidden Markov Model. In: van den Herik, J., Rocha, A., Steels, L. (eds) Agents and Artificial Intelligence. ICAART 2019. Lecture Notes in Computer Science(), vol 11978. Springer, Cham. https://doi.org/10.1007/978-3-030-37494-5_3

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  • DOI: https://doi.org/10.1007/978-3-030-37494-5_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37493-8

  • Online ISBN: 978-3-030-37494-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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