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Improving Matching Process with Expanding and Classifying Criterial Keywords leveraging Word Embedding and Hierarchical Clustering Methods

Author

Listed:
  • Yutaka Iwakami

    (Nork Research Co., Ltd)

  • Hironori Takuma

    (Chiba Institute of Technology)

  • Motoi Iwashita

    (Chiba Institute of Technology)

Abstract

Matching processes, such as the selection of producers of advertising content corresponding to specific products or the screening of job applicants based on predefined requirements, have become important operations required by enterprises. Such problems generally include several keywords representing the matching criteria, but it is difficult for enterprises to expand and classify criterial keywords properly to improve the matching performance. This study proposes solutions to this issue by extracting criterial keywords from social networking services (SNSs) based on word embedding and by classifying the obtained keywords via hierarchical clustering. This approach will enable enterprises to gather and prioritize criterial keywords more accurately to improve their matching processes.

Suggested Citation

  • Yutaka Iwakami & Hironori Takuma & Motoi Iwashita, 2020. "Improving Matching Process with Expanding and Classifying Criterial Keywords leveraging Word Embedding and Hierarchical Clustering Methods," The Review of Socionetwork Strategies, Springer, vol. 14(2), pages 193-204, October.
    • Handle: RePEc:spr:trosos:v:14:y:2020:i:2:d:10.1007_s12626-020-00063-4
    DOI: 10.1007/s12626-020-00063-4
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    References listed on IDEAS

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    1. Higashi, Yudai, 2018. "Spatial spillovers in job matching: Evidence from the Japanese local labor markets," Journal of the Japanese and International Economies, Elsevier, vol. 50(C), pages 1-15.
    2. Den Haan, Wouter J. & Kaltenbrunner, Georg, 2009. "Anticipated growth and business cycles in matching models," Journal of Monetary Economics, Elsevier, vol. 56(3), pages 309-327, April.
    3. Robert E. Hall & Sam Schulhofer-Wohl, 2018. "Measuring Job-Finding Rates and Matching Efficiency with Heterogeneous Job-Seekers," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(1), pages 1-32, January.
    4. Chakraborty, Saptarshi & Paul, Debolina & Das, Swagatam, 2020. "Hierarchical clustering with optimal transport," Statistics & Probability Letters, Elsevier, vol. 163(C).
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