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Analysis of the Exposing Media Pattern that Affect Accessing Own Website

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Book cover Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12194))

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Abstract

Recently, due to the expansion of TV or smart phone, the frequency of exposing media has increased. Also, the way of interacting with the media is diversifying by life stages or life balance. In such situations, people get much information about products and services on media. Therefore, it is important to select the best media for advertisement. In this study, we analyze the characteristics of exposing media using media exposure data. First, we performed Non-negative Matrix Factorization (NMF) to extract pattern of exposing media. Second, we used random forest to analyze the characteristics of the exposing media pattern. From our result, we discussed how to advertise on TV and website.

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Notes

  1. 1.

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Acknowledgment

We thank INTAGE Holdings Inc. for permission to use valuable datasets and for useful comments. This work was supported by JSPS KAKENHI Grant Number 19K01945 and 17K13809.

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Authors and Affiliations

  1. Graduate School of Science and Engineering, Chuo University, 1-13-27, Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan

    Yuho Katagiri

  2. School of Information and Telecommunication Engineering, Tokai University, 2-3-23, Takanawa, Minato-ku, Tokyo, 108-8619, Japan

    Kohei Otake

  3. Faculty of Science and Engineering, Chuo University, 1-13-27, Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan

    Takashi Namatame

Authors
  1. Yuho Katagiri
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  2. Kohei Otake
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  3. Takashi Namatame
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Corresponding author

Correspondence to Yuho Katagiri .

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Editors and Affiliations

  1. Towson University, Towson, MD, USA

    Gabriele Meiselwitz

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Katagiri, Y., Otake, K., Namatame, T. (2020). Analysis of the Exposing Media Pattern that Affect Accessing Own Website. In: Meiselwitz, G. (eds) Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis. HCII 2020. Lecture Notes in Computer Science(), vol 12194. Springer, Cham. https://doi.org/10.1007/978-3-030-49570-1_39

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  • DOI: https://doi.org/10.1007/978-3-030-49570-1_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49569-5

  • Online ISBN: 978-3-030-49570-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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