이선경 박사과정, 박성민 석박통합과정, 김종효 학사과정, 윤민철 박사과정 EMNLP 2025 Findings 국제 학술대회 논문 채택

21 Aug 2025

DIAL 연구실 소속 인공지능학과 이선경 학생 (박사과정, 제1저자), 박성민 학생 (석박통합과정 제2저자), 김종효 학생 (학사과정, 제3저자), 윤민철 (박사과정, 제4저자), 이종욱(교신저자) 교수가 참여한 논문 "Enhancing Time Awareness in Generative Recommendation"이 자연어처리 분야 최우수 국제 학술대회인 The 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP Findings 2025)에 게재 승인되었으며 오는 11월에 발표될 예정입니다. 

Abstract

Generative recommendation has emerged as a promising paradigm that formulates the recommendations into a text-to-text generation task, harnessing the vast knowledge of large language models. However, existing studies focus on considering the sequential order of items and neglect to handle the temporal dynamics across items, which can imply evolving user preferences. To address this limitation, we propose a novel model, Generative Recommender Using Time awareness (GRUT), effectively capturing hidden user preferences via various temporal signals. We first introduce Time-aware Prompting, consisting of two key contexts. The user-level temporal context models personalized temporal patterns across timestamps and time intervals, while the item-level transition context provides transition patterns across users. We also devise Trend-aware Inference, a training-free method that enhances rankings by incorporating trend information about items with generation likelihood. Extensive experiments demonstrate that GRUT outperforms state-of-the-art models, with gains of up to 30.0% and 24.8% in Recall@5 and NDCG@5 across four benchmark datasets.