Kairos: Numerically Robust News Recommendation under Item Cold-Start via Cholesky-based LinUCB

Abstract: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.

English preprint. The German version was peer-reviewed and accepted at SKILL 2026 (Gesellschaft für Informatik)

Machine Learning (cs.LG); Information Retrieval (cs.IR)

arXiv:2607.26832 [cs.LG]

(or arXiv:2607.26832v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.26832 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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