Extrapolative Collaborative Filtering 실험 : 컨텐트 서비스간 협력 환경 적용

배성원조은별정백황보유정이경전 (2021). Extrapolative Collaborative Filtering 실험 : 컨텐트 서비스간 협력 환경 적용. 한국경영정보학회 학술대회, 568-571.

Abstract

본 논문은 Extrapolative Collaborative Filtering 방법론의 유용성을 컨텐트 추천 분야에서 확인하고자 한다. 컨텐트 서비스들은 개별적으로 운영되고, 그들간의 정보 공유가 이루어지지 않아, 타 서비스를 이용할 경우 다른 서비스에서의 자신의 성향이 반영되지 않는 한계가 있다. 이에 본 연구에서는 다양한 컨텐트 사이트를 이용하는 사용자의 편의를 위해 타 사이트간의 최소한의 협력이 있는 환경에서, 상호적 추천을 하는 Extrapolative Collaborative Filtering (ECF) 방법론이 유용한지를 검증하였다. 이를 위해 공개된 4개의 컨텐트 사이트 데이터셋을 확보하여 실험하였다. 그 결과, 적절한 협력을 하는 경우가 독자적으로 하는 것보다 추천의 성과가 향상될 수 있음을 확인하였다.

Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs

Kyoung Jun Lee, YuJeong Hwangbo, Baek Jeong, Jiwoong Yoo, & Kyung Yang Park. (2021). Extrapolative Collaborative Filtering Recommendation System with Word2Vec for Purchased Product for SMEs. Sustainability13(13), 7156. (Pdf)

Abstract

Many small and medium enterprises (SMEs) want to introduce recommendation services to boost sales, but they need to have sufficient amounts of data to introduce these recommendation services. This study proposes an extrapolative collaborative filtering (ECF) system that does not directly share data among SMEs but improves recommendation performance for small and medium-sized companies that lack data through the extrapolation of data, which can provide a magical experience to users. Previously, recommendations were made utilizing only data generated by the merchant itself, so it was impossible to recommend goods to new users. However, our ECF system provides appropriate recommendations to new users as well as existing users based on privacy-preserved payment transaction data. To accomplish this, PP2Vec using Word2Vec was developed by utilizing purchase information only, excluding personal information from payment company data. We then compared the performances of single-merchant models and multi-merchant models. For the merchants with more data than SMEs, the performance of the single-merchant model was higher, while for the SME merchants with fewer data, the multi-merchant model’s performance was higher. The ECF System proposed in this study is more suitable for the real-world business environment because it does not directly share data among companies. Our study shows that AI (artificial intelligence) technology can contribute to the sustainability and viability of economic systems by providing high-performance recommendation capability, especially for small and medium-sized enterprises and start-ups.

Eunbyul Diana Cho

Eunbyul Diana Cho is working as a researcher in AI-BM Lab since 2021. She is interested in Artificial Intelligence. She has B.A in International Hotel Management, Sejong University in S. Korea and A.H.L.A in Canada.