Author

J. Tan

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#artificial intelligence Preprint Sep 2026

Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself

This paper trains a recommender LLM to generate personalized explanations for its reccomendation, based on the user's watching history at a large video streaming service, and concludes that an LLM-based recommender can be fine-tuned on other complex tasks without compromising its original recommendation performance.

Jia-Shu He, Emma Kong, J. Tan et al. · 0 citations

#artificial intelligence Review Aug 2026

The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations

This work argues that an LLM judge running in a production system is better understood as having a lifecycle: it must be built, trained, deployed, and continuously maintained as the surrounding data evolves, and each phase poses distinct technical and operational challenges.

Emma Kong, J. Tan, Ishan Gupta et al. · 0 citations