Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation

• URL: http://arxiv.org/abs/2508.13064v1
• Date: Mon, 18 Aug 2025 16:36:27 GMT
• Title: Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
• Authors: Seongeun Ryu, Yunyong Ko, Sang-Wook Kim,
• Abstract summary: We propose a novel Lifetime-aware Interest Matching framework for nEws recommendation, named LIME.<n>LIME 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.
• Score: 17.100667350463464
• License: http://creativecommons.org/licenses/by/4.0/
• 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 LIME consistently outperforms a wide range of state-of-the-art news recommendation methods, and its model agnostic strategies significantly improve recommendation accuracy.

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