Earticle

초록

영어

To investigate how training data bias impacts recommendation quality, this study experimented with simulated data to observe changes in recommendation performance and result bias across varying levels of data bias. The experiment revealed that while imbalances in popularity and preference distributions within the training data did not significantly affect recommendation accuracy, they did strongly influence recommendation result bias, leading to amplification effects. Specifically, increased preference distribution imbalance intensified recommendation result bias, whereas a certain level of popularity imbalance had no effect on recommendation result bias nor caused amplification. These findings challenge previous research suggesting that data bias degrades recommendation accuracy but support claims that data bias amplifies recommendation result bias. This study is notable for providing insights into how data bias mechanisms impact recommendation quality, offering valuable guidance for future research focused on bias mitigation.

목차

Abstract
1. 서론
2. 기존연구
3. 연구문제 및 연구방법
3.1 연구문제
3.2 측정지표
4. 추천 모형
5. 실험
5.1 훈련데이터 생성
6. 결론
References

키워드

저자

• 오소진 [ So Jin Oh | Professor, Hannam University, Department of MIS ] First Author
• 송희석 [ Hee Seok Song | Professor, Hannam University, Department of MIS ] Corresponding Author

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