🤖 AI Summary
This work addresses the item cold-start problem in regional news recommendation, which arises from short news lifecycles and shallow article pools. To tackle this challenge, the authors propose an enhanced LinUCB online contextual recommendation method that replaces the Sherman–Morrison formula with a rank-1 update mechanism based on Cholesky decomposition, thereby improving the numerical stability and robustness of covariance matrix estimation. Additionally, Matryoshka representation learning is integrated to reduce inference latency. Experimental results using the Tagesschau API demonstrate that the proposed approach achieves a favorable trade-off between efficiency and accuracy under data-scarce conditions, delivering a 4.85× speedup in inference with negligible degradation in ranking performance—making it well-suited for environments constrained by limited computational resources and sparse data.
📝 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.