Kairos：基于Cholesky的LinUCB的项目冷启动下数值鲁棒的新闻推荐
Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB
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中文总结 AI 辅助
Kairos项目提出基于Cholesky的LinUCB框架，结合MRL缓解新闻推荐的项目冷启动问题，提升数值鲁棒性与效率，在Tagesschau API评估中获4.85倍效率增益且精度未显著下降。
中文摘要 AI 辅助
区域市场的算法新闻个性化推荐常失效，因为现代深度学习模型需大量交互数据，而现实中新闻生命周期短（TTL<48小时）、文章池浅，这种结构性项目冷启动剥夺了协同过滤进行鲁棒建模所需的数据。本文提出Project Kairos框架，通过上下文在线学习方法（LinUCB）缓解数据稀缺；为确保持续运行的数值完整性，Kairos用Cholesky因子的直接秩1更新替代易出错的Sherman-Morrison求逆，即使在病态数据场景下也能保持协方差矩阵的正定性；同时集成Matryoshka Representation Learning（MRL）解决推理延迟问题。基于Tagesschau API的实证评估显示，利用特征空间的语义冗余实现了4.85倍的效率提升，且未显著降低排序精度，为资源与数据受限环境下的高性能推荐系统提供了蓝图。
英文摘要
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.
发表机构
- DHBW Ravensburg（德国巴登符腾堡州双元制大学拉芬斯堡分校）
- School of Business, Data Science and AI（商学院、数据科学与人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。
