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Analysis of Fashion Market Trend Using Advertising Data of Shopping Information Site

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Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing (HCII 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12195))

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Abstract

In recent years, consumer needs for products have been diversified, and companies need proper site management considering trends. As the needs of consumers diversified, it became difficult to grasp the needs. In this study, we analyzed diversifying customer needs by focusing on internet advertising. We classify it into a company’s products by Doc2Vec using each shopping site documents to segment the market using word similarity in the fashion market and verify that a company can accurately identify the needs of consumers in fashion sites. Afterwards, we consider the impression tendency of sites with similar ones by using time-series clustering.

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Acknowledgment

We thank Feedforce 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

    Retsuya Saito

  2. Faculty of Science and 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. Retsuya Saito
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  2. Kohei Otake
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  3. Takashi Namatame
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Corresponding author

Correspondence to Retsuya Saito .

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Editors and Affiliations

  1. Towson University, Towson, MD, USA

    Gabriele Meiselwitz

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Saito, R., Otake, K., Namatame, T. (2020). Analysis of Fashion Market Trend Using Advertising Data of Shopping Information Site. In: Meiselwitz, G. (eds) Social Computing and Social Media. Participation, User Experience, Consumer Experience, and Applications of Social Computing. HCII 2020. Lecture Notes in Computer Science(), vol 12195. Springer, Cham. https://doi.org/10.1007/978-3-030-49576-3_28

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  • DOI: https://doi.org/10.1007/978-3-030-49576-3_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-49575-6

  • Online ISBN: 978-3-030-49576-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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