考虑新闻时效性与用户兴趣持久度，提升推荐精准度。
Is This News Still Interesting to You?: Lifetime-aware Interest Matching for News Recommendation
- 用用户-话题的相对年龄建模兴趣持久性
- 在真实数据集上显著优于现有方法
- 适合关注时效推荐与用户长期兴趣的场景
个性化新闻推荐旨在将符合用户兴趣的文章推送给用户，是缓解在线新闻平台信息过载的关键方案。尽管已有研究通过优化新闻和用户表征提升了兴趣匹配效果，但以下两个时间相关挑战仍被忽视：(C1) 利用已点击新闻的年龄来推断用户兴趣的持续性；(C2) 建模不同主题和用户间新闻生命周期的差异。为此，我们提出一种新的新闻推荐框架LIME（Lifetime-aware Interest Matching for nEws recommendation），包含三项关键策略：(1) 用户-话题寿命感知的年龄表示，捕捉新闻相对于用户-话题对的相对年龄；(2) 候选新闻感知的寿命注意力机制，生成时间对齐的用户表征；(3) 新鲜度引导的兴趣精炼机制，在预测时优先筛选有效候选新闻。在两个真实世界数据集上的大量实验表明，LIME持续优于多种先进推荐方法，其模型无关策略显著提升了推荐精度。
原文摘要 · Abstract (English)
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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