[발표] 석사과정생 이동준 학생(지도교수: 김재광) 논문 SIGIR 2023, the Perspective paper track에 채택
- 소셜이노베이션융합전공
- Hit337
- 2023-04-17
main Lab. (지도교수: 김재광)의 논문 “How Important is Periodical Model update in Recommender System?” 이 세계 최고 권위 정보검색(Information Retrieval) 학회인 SIGIR 2023 (The 46th International ACM SIGIR Conference on Research and Development in Information Retrieval), the Perspective paper track에 채택되었습니다. 논문은 23년 7월 대만 타이페이에서 발표될 예정입니다.
본 논문은 카카오 추천팀과 협력한 연구로 추천시스템에서 주기적인 모델 업데이트의 중요성에 대한 온/오프라인 분석을 하여 특정 관점에서 의미있는 결과들을 도출하였습니다. 본 연구에는 성균관대학교 전기전자컴퓨터공학과의 석사과정 이동준 학생이 공동저자로 참여하였고, 김재광 교수가 교신저자로 참여하였습니다.
[논문] H. Lee, S. Yoo, D. Lee, and J. Kim, “How Important is Periodical Model update in Recommender System?,” In Proceedings of 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2023), July 2023.
[Abstract] In real-world recommender model deployments, the models are typically retrained and deployed repeatedly. It is the rule-of-thumb to periodically retrain recommender models to capture up-to-date user behavior and item trends. However, the harm caused by delayed model updates has not been investigated extensively yet. in this perspective paper, we formulate the delayed model update problem and quantitatively demonstrate the delayed model update actually harms the model performance by increasing the number of cold users and cold items increase and decreasing overall model performances. These effects vary across different domains having different characteristics. Upon these findings, we further argue that although the delayed model update has negative effects on online recommender model deployment, yet it has not gathered enough attention from research communities. We argue our verification of the relationship between the model update cycle and model performance calls for further research such as faster model training, and more efficient data pipelines to keep the model more up-to-date with the latest user behaviors and item trends.