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

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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.

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Authors and Affiliations

  1. Department of Research & Analysis for IT Industry, Nork Research Co., Ltd, 2-13-10 Shinjuku, Shinjuku-Ku, Tokyo, 160-0022, Japan

    Yutaka Iwakami

  2. Department of Project Management, Chiba Institute of Technology, 2-17-1 Tsudanuma Narashino, Chiba, 275-0016, Japan

    Hironori Takuma

  3. Department of Management Information Science, Chiba Institute of Technology, 2-17-1 Tsudanuma Narashino, Chiba, 275-0016, Japan

    Motoi Iwashita

Authors
  1. Yutaka Iwakami
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  2. Hironori Takuma
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  3. Motoi Iwashita
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Correspondence to Yutaka Iwakami.

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Iwakami, Y., Takuma, H. & Iwashita, M. Improving Matching Process with Expanding and Classifying Criterial Keywords leveraging Word Embedding and Hierarchical Clustering Methods. Rev Socionetwork Strat 14, 193–204 (2020). https://doi.org/10.1007/s12626-020-00063-4

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