류성은/고윤용’s paper has been accepted in

Title: Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
Author: Seongeun Ryu, Yunyong Ko and Sang-Wook Kim
Abstract
Personalized news recommendation aims to deliver news articles aligned with users’ interests, serving as a key solution to alleviate the problem of information overload on online news platforms. While prior work has improved interest matching through refined representations of news and users, the following time-related challenges remain underexplored: (C1) leveraging the age of clicked news to infer users’ interest persistence, and (C2) modeling the varying lifetime of news across topics and users. To jointly address these challenges, we propose a novel lifetime-aware interest matching framework for news recommendation, named LIME, which incorporates three key strategies: (1) User-Topic lifetime-aware age representation to capture the relative age of news with respect to a user-topic pair, (2) Candidate-aware lifetime attention for generating temporally aligned user representation, and (3) Freshness-guided interest refinement for prioritizing valid candidate news at prediction time. Extensive experiments on two real-world datasets demonstrate that (1) LIME consistently outperforms a wide range of state-of-the-art news recommendation methods, and (2) its model-agnostic strategies significantly improve recommendation accuracy.