발간년도 : [2021]
| 논문정보 |
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| 논문명(한글) |
[Vol.16, No.3] An Incremental Recommendation Technique to Improve Product Recommendation Accuracy |
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| 논문투고자 |
Chul-Jin Kim, Ji-Hyun Jeong, Cheon-Woo Jo |
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| 논문내용 |
In order to recommend products of interest in existing e-commerce transactions, recommended products are derived through CRM or big data analysis, or products are recommended using a recommendation technique applied with machine learning technology. Among the existing recommendation techniques, a recommendation technique using a collaborative filtering technique or machine learning technique has had a great influence on improving the purchasing power of a product to users. However, the purchase data generated by the recommendation data provided to the customer by the recommendation model generated through various recommendation techniques is meaningful as data for generating more reliable recommendations for purchases that will occur in the future, but was not used. Therefore, in this research, in order to improve the accuracy of e-commerce recommendation, we use customer purchase data generated through data for product recommendation, and propose an incremental recommendation technique based on this. Based on the LSTM model, we propose the architecture and procedure of the incremental recommendation technique. In the experiment, to verify the proposed architecture, we learn using the published e-commerce data and verify the accuracy through the separated data. Also, the accuracy of the technique proposed in this study is compared and analyzed with the existing recommended techniques. |
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