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Visualizing Switching Regimes Based on Multinomial Distribution in Buzz Marketing Sites

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Foundations of Intelligent Systems (ISMIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10352))

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Abstract

The review scoring results in large-scale buzz marketing sites can greatly affect actual purchase activities of many users. In this paper, since the scoring tendency for an item usually changes over time due to several reasons, we propose a method for visualizing its scoring stream data as a timeline based on switching regimes. Namely, by assuming that fundamental scoring behavior of users in each regime obeys a multinomial distribution model, we first estimate the switching time steps and the model parameters by maximizing the likelihood of generating the observed scoring stream data, and then produce a timeline and its associated dendrogram as our final visualization results by calculating the probability function from the estimated switching regimes. In our experiments using not only synthetic stream data generated from a known ground truth model but also real scoring stream data collected from a Japanese buzz marketing site, we show that our proposed method can produce accurate and interpretable visualization results for such stream data.

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Acknowledgments

This work was supported by JSPS Grant-in-Aid for Scientific Research (C) (No. 16J11909).

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Authors and Affiliations

  1. Graduate School of Management and Information of Inovation, University of Shizuoka, 52-1 Yada, Shizuoka, Suruga, 422-8526, Japan

    Yuki Yamagishi & Kazumi Saito

Authors
  1. Yuki Yamagishi
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  2. Kazumi Saito
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Corresponding author

Correspondence to Yuki Yamagishi .

Editor information

Editors and Affiliations

  1. Warsaw University of Technology, Warsaw, Poland

    Marzena Kryszkiewicz

  2. University of Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  3. Institute of Informatics, University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  4. Faculty of Electronics & Information, Warsaw University of Technology, Warsaw, Poland

    Henryk Rybinski

  5. Institute of Mathematics, Warsaw University, Warsaw, Poland

    Andrzej Skowron

  6. Department of Computer Science, University of North Carolina at Charlotte, North Carolina, USA

    Zbigniew W. RaÅ›

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Yamagishi, Y., Saito, K. (2017). Visualizing Switching Regimes Based on Multinomial Distribution in Buzz Marketing Sites. In: Kryszkiewicz, M., Appice, A., Ślęzak, D., Rybinski, H., Skowron, A., Raś, Z. (eds) Foundations of Intelligent Systems. ISMIS 2017. Lecture Notes in Computer Science(), vol 10352. Springer, Cham. https://doi.org/10.1007/978-3-319-60438-1_38

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  • DOI: https://doi.org/10.1007/978-3-319-60438-1_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60437-4

  • Online ISBN: 978-3-319-60438-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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