Earticle

초록

영어

As deep learning technology in natural language and visual processing has rapidly developed, collaborative filtering-based recommendation systems using deep learning technology are being actively introduced in the recommendation field. In this study, OCF-GAN, a hybrid collaborative filtering model using GAN, was proposed to solve the one-class and cold-start problems, and its usefulness was verified through performance evaluation. OCF-GAN based on conditional GAN consists of a generator that generates a pattern similar to the actual user preference pattern and a discriminator that tries to distinguish the actual preference pattern from the generated preference pattern. When the training is completed, user preference vectors are generated based on the actual distribution of preferred items. In addition, the cold-start problem was solved by using a hybrid collaborative filtering recommendation method that additionally utilizes user and item profiles. As a result of the performance evaluation, it was found that the performance of the OCF-GAN with additional information was superior in all indicators of the Top 5 and Top 20 recommendations compared to the existing GAN-based recommender. This phenomenon was more clearly revealed in experiments with cold-start users and items.

목차

Abstract
1. 서론
2. 기존연구
2.1 원클래스 문제를 다룬 기존연구
2.2 하이브리드 협업필터링 연구
3. OCF-GAN(One-class and Cold-start problem Free GAN) 추천기 설계
4. 실험
4.1 데이터셋
4.2 성능평가 방법
4.3 실험설계 및 실험환경
4.4 실험결과
5. 결론 및 향후 연구
References

키워드

저자

• 송희석 [ Hee Seok Song | Professor, Hannam University, Department of MIS, 70 Hannamro Daeduckgu Daejeon 34430, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  JITAM [Journal of Information Technology Applications and Management]

• 간기

  격월간

• pISSN

  1598-6284

• eISSN

  2508-1209

• 수록기간

  1999~2026

• 십진분류

  KDC 005 DDC 005