Journal of Information Technology Applications and Management

  • 제23권4호
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  • Pages.55-70
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  • 2016
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  • 1598-6284(pISSN)
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  • 2508-1209(eISSN)
한국데이터전략학회 (Korea Data Strategy Society)
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오프라인 쇼핑몰에서 고객 행위에 기반을 둔 맞춤형 브랜드 추천에 관한 연구

A Study on Customized Brand Recommendation based on Customer Behavior for Off-line Shopping Malls

  • 투고 : 2016.10.18
  • 심사 : 2016.11.30
  • 발행 : 2016.12.31
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초록

Recently, development of indoor positioning system and IoT such as beacon makes it possible to collect and analyze each customer's shopping behavior in off-line shopping malls. In this study, we propose a realtime brand recommendation scheme based on each customer's brand visiting history for off-line shopping mall with indoor positioning system. The proposed scheme, which apply collaborative filtering to off-line shopping mall, is composed of training and apply process. The training process is designed to make the base brand network (BBN) using historical transaction data. Then, the scheme yields recommended brands for shopping customers based on their behaviors and BBN in the apply process. In order to verify the performance of the proposed scheme, simulation was conducted using purchase history data from a department store in Korea. Then, the results was compared to the previous scheme. Experimental results showd that the proposed scheme performs brand recommendation effectively in off-line shopping mall.

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참고문헌

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피인용 문헌

  1. 오프라인 맞춤형 광고 제공을 위한 오픈소스 로봇 플랫폼 vol.10, pp.4, 2016, https://doi.org/10.22156/cs4smb.2020.10.04.001