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An Open Architecture and Open API for e-Commerce Recommendation Model Development Platform

  • Journal of Knowledge Information Technology and Systems
  • Abbr : JKITS
  • 2020, 15(6), pp.991-1000
  • DOI : 10.34163/jkits.2020.15.6.008
  • Publisher : Korea Knowledge Information Technology Society
  • Research Area : Interdisciplinary Studies > Interdisciplinary Research
  • Received : October 19, 2020
  • Accepted : December 11, 2020
  • Published : December 31, 2020

Kim Chul Jin 1,  Jeong， JiHyun 1,  Cheon-Woo Jo 1,  ByunDongHun 1

1인하공업전문대학

Accredited

ABSTRACT

e-Commerce recommendation service is an essential function and plays an important role in increasing sales since it is provided in connection with functions such as product search, order processing, and shopping cart. This recommendation service requires a high level of technology from developers developing e-commerce, so it is developed by a specific artificial intelligence engineer or applied by introducing an external recommendation solution. Integration of recommended services by external solutions or external development companies cannot satisfy the requirements of e-commerce services to be developed, and cannot provide rapid maintenance due to frequent data changes. Accordingly, research on a generalized development platform for generalizing and providing recommendation services suitable for a specific domain or developing a recommendation service is being actively conducted. Amazon Personalize service and Microsoft Azure Machine Learning service are generalized tools for developing recommended services by developers. However, these recommendation model development tools have a workload of defining essential data information for training data required to generate a recommendation model. In this paper, we derive a learning algorithm without defining data by using an association analysis algorithm between data for analyzing learning data. Also, based on the derived learning algorithm, we propose an Open API for developing and verifying a recommendation model. In the experiment, the learning algorithm is derived and the open API is verified by using the open transaction data of the e-commerce transaction. Through this, the suitability of the open architecture of the recommendation model development platform is verified.

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This paper was written with support from the National Research Foundation of Korea.
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