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정지현,조천우,변동훈,김철진 한국지식정보기술학회 2021 한국지식정보기술학회 논문지 Vol.16 No.1
Current sales of offline products are conducted on a face-to-face recommendation based on customer’s preferences and fields of interest. However, as the online shopping malls are actively spreading to the public, recommendation researches are ongoing to make more accurate recommendation using customers’ online activity data besides a face-to-face recommendation. But compared to recommendation systems of large-scale shopping mall where operates big-scale database and professional human resources, small-scale shopping malls use simple recommenders relatively, which can be interpreted as a judgement that those recommenders developed by large-scale companies are unfit to apply onto their system. In this paper, we propose a platform that can develop a generalized recommendation tool by utilizing Open API. Recommender-development tool of this study refines datasets to be used for developing a recommendation system, analyzes the datasets to derive proper algorithms. After doing those progresses, it proceeds model learning progress based on the derived algorithm and provides Open API to a user so that the user can apply the recommendation system to own shopping mall system. We prove proposed tool by developing recommendation system using real transaction datasets with this tool, lastly mounting the Open API onto the test shopping mall site. While users using existing tools must be directly specified the schema of datasets to make a recommendation system, this study has a distinction in that it allows users to develop the recommendation model by automatically proceeding with this process.
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전자상거래 추천 모델 개발 플랫폼을 위한 오픈 아키텍쳐 및 Open API
김철진,정지현,조천우,변동훈 한국지식정보기술학회 2020 한국지식정보기술학회 논문지 Vol.15 No.6
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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