발간년도 : [2021]
| 논문정보 |
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| 논문명(한글) |
[Vol.16, No.1] A Design and Implementation of Recommendation Learning Model Generation Tool Based on Data Analysis |
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| 논문투고자 |
Ji-Hyun Jeong, Cheon-Woo Jo, Dong-Hun Byun, Chul-Jin Kim |
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| 논문내용 |
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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