상품 추천 정확도를 향상시키기 위한 점증적 추천 기법
An Incremental Recommendation Technique to Improve Product Recommendation Accuracy
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초록
기존 전자상거래에서 관심 상품을 추천하기 위해 CRM이나 빅데이터 분석을 통해 추천 상품을 도출하거나, 머신러닝 기술을 적용한 추천 기법을 활용하여 상품을 추천한다. 기존의 추천 기법들 중, 협업적 필터링 기법이나 머신러닝 기술을 활용한 추천 기법은 사용자에게 상품의 구매력을 향상시키는데 많은 영향을 주었다. 그러나, 다양한 추천 기법을 통해 생성된 추천 모델이 고객에게 제공한 추천 데이터로 인해 발생한 구매 데이터는 추후 발생할 구매에 대해 더욱 신뢰성 있는 추천을 생성하기 위한 데이터로 의미가 있으나 활용되지 않았다. 이에 본 연구에서는 전자상거래의 추천의 정확성을 향상시키고자 상품 추천을 위한 데이터를 통해 발생된 고객의 구매 데이터를 활용하며 이를 기반한 점증적 추천 기법을 제안한다. LSTM 모델을 기반으로 점증적 추천 기법의 아키텍쳐 및 절차를 제안한다. 실험에서는 제안된 아키텍쳐를 검증하기 위해 공개된 전자상거래 데이터를 이용하여 학습하며 분리된 데이터를 통해 정확성을 검증한다. 또한 본 연구에서 제안한 기법의 정확도를 기존의 추천 기법들과 비교 분석한다.
Abstract
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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