백일웅 석박통합과정, 윤민철 박사과정, 박성민 석박통합과정 CIKM 2025 국제 학술대회 논문 채택

5 Aug 2025

DIAL 연구실 소속 인공지능학과 백일웅(석박통합과정, 공동 제1저자) 학생, 윤민철(박사과정, 공동 제1저자) 학생, 박성민(석박통합과정, 제3저자) 학생, 이종욱(교신저자) 교수가 참여한 논문 "MUFFIN: Mixture of User-Adaptive Frequency Filtering for Sequential Recommendation"이 데이터마이닝 분야 최우수 국제 학술대회인 ACM International Conference on Information and Knowledge Management (CIKM 2025)에 최종 게재가 승인되었으며 오는 11월에 발표될 예정입니다. 

Abstract

Sequential recommendation (SR) aims to predict users’ subsequent interactions by modeling their sequential behaviors. Recent studies have explored frequency domain analysis, which effectively models periodic patterns in user sequences. However, existing frequency-domain SR models still face two major drawbacks: (i) limited frequency band coverage, often missing critical behavioral patterns in a specific frequency range, and (ii) lack of personalized frequency filtering, as they apply an identical filter for all users regardless of their distinct frequency characteristics. To address these challenges, we propose a novel frequency-domain model, Mixture of User-adaptive Frequency FIlteriNg (MUFFIN), operating through two complementary modules. (i) The global filtering module (GFM) handles the entire frequency spectrum to capture comprehensive behavioral patterns. (ii) The local filtering module (LFM) selectively emphasizes important frequency bands without excluding information from other ranges. (iii) In both modules, the user-adaptive filter (UAF) is adopted to generate user-specific frequency filters tailored to individual unique characteristics. Finally, by aggregating both modules, MUFFIN captures diverse user behavioral patterns across the full frequency spectrum. Extensive experiments show that MUFFIN consistently outperforms state-of-the-art frequency-domain SR models, achieving up to 10.06% gains in NDCG@5, over five benchmark datasets.