Kairos Boosts News Recommendation in Data-Scarce Markets

Project Kairos presents a novel framework designed to address the challenges of news personalization in regional markets, particularly where data scarcity and short article lifecycles (item cold-start) hinder traditional deep learning models. The core of Kairos is a contextual online learning approach, LinUCB, enhanced with Cholesky-based rank-1 updates. This enhancement ensures numerical stability and preserves the positive definiteness of the covariance matrix, even when dealing with ill-conditioned data, which is common in dynamic news environments. Furthermore, Kairos integrates Matryoshka Representation Learning (MRL) to reduce inference latency. Empirical evaluations using the Tagesschau API demonstrate that by leveraging semantic redundancy in the feature space, Kairos achieves a 4.85-fold efficiency gain without compromising ranking precision. This makes Kairos a viable solution for deploying high-performance news recommendation systems in environments with limited data and computational resources.