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.
The phenomenon of information overload on digital platforms frequently leads to a skewed long-tail distribution, where user interactions concentrate on popular items while the majority remains undiscovered. This popularity bias is particularly acute in the book domain, where metaphorical titles defy traditional lexical...
Andy Maulana Yusuf, Miftah Farid Adiwisastra, Faizal Riza et al.· JITK (Jurnal Ilmu Pengetahua...· 0 citations
The findings support Proxy Theory by indicating that review volume and valence can provide leading information before sufficient transaction data accumulate and inform replenishment and safety-stock decisions by reducing underforecasting during demand activation.
I. M. Siregar, I. Fauzi· Journal of Business, Social...· 0 citations
The Fusion of Layer-wise Exits for Sequential Recommendation (FLEXRec), a discriminative framework that enhances compact LLMs while retaining scalable full-corpus ranking and achieves state-of-the-art accuracy among competing methods while remaining highly efficient.
Xurong Liang, Tong Chen, Q. Nguyen et al.· 0 citations
This work proposes a decorrelation-regularized training framework that augments next-item prediction with an auxiliary redundancy-reduction term, and instantiate it with BT-SR, which uses the Barlow Twins objective to form label-consistent positive pairs without synthetic corruptions.
Veronika Ivanova, Marina Munkhoeva, Ivan Razvorotnev et al.· Proceedings of the 20th ACM...· 0 citations
This work proposes TUP: a Truncate-bad, Upweight-good Policy that removes low-ranked completions from the support and reweights only the retained upper tail with a tunable sharpness, and shows that TUP is competitive with strong offline alignment baselines.
Industrial ranking systems often rely on sorting by predicted Click-Through Rate (CTR). However, current CTR models heavily depend on features derived from user behavioral signals and struggle to accurately rank cold items in the case of insufficient user behavioral data. To address this challenge, we introduce UCB-FE,...
Anastasiia Soboleva, Andrey Pudovikov, Aleksandr Katrutsa et al.· Proceedings of the 32nd ACM...· 0 citations
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