解决新闻推荐冷启动问题，用高效算法提升数据稀缺下的推荐效果。
Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
- 采用基于Cholesky分解的LinUCB方法，避免数值误差。
- 在真实新闻数据上实现4.85倍效率提升，精度损失小。
- 适合资源有限、数据少的实时推荐场景使用。
区域市场中的算法化新闻推荐常因现代深度学习模型依赖大量交互数据而失效，而真实新闻具有短生命周期（TTL < 48小时）和小文章池的特点，导致协同过滤难以建模。本文提出Kairos框架，通过上下文在线学习方法（LinUCB）缓解数据稀缺问题。为保障长期运行的数值稳定性，Kairos用Cholesky因子的秩1更新替代易出错的Sherman-Morrison反演，确保协方差矩阵在病态数据下仍保持正定性。同时，引入马特罗什卡表示学习（MRL）以降低推理延迟。基于Tagesschau API的实证评估表明，在特征空间中利用语义冗余可实现4.85倍的效率提升，且排名精度未显著下降。Kairos为资源与数据受限环境下的高性能推荐系统提供了可行方案。
原文摘要 · Abstract (English)
Algorithmic news personalization in regional markets often fails because modern deep learning models require massive interaction data while real-world news has a short Time-to-Live (TTL < 48 h) and shallow article pools. This structural item cold-start deprives collaborative filtering of the data needed for robust modeling. This paper presents Project Kairos, a framework that bridges this data scarcity through a contextual online learning approach (LinUCB). To ensure numerical integrity for continuous operation, Kairos replaces error-prone Sherman-Morrison inversions with direct rank-1 updates of Cholesky factors. This preserves the positive definiteness of the covariance matrix even under ill-conditioned data scenarios. Simultaneously, Matryoshka Representation Learning (MRL) integration addresses inference latency. Empirical evaluations based on the Tagesschau API demonstrate that exploiting semantic redundancy in the feature space achieves a 4.85-fold efficiency gain without significantly compromising ranking precision. Kairos thus provides a blueprint for high-performance recommendation systems in resource- and data-constrained environments.
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