4 papers
Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself
Jiashu He, Emma Yanyang Kong, JJ Tan +1
Traditional recommender systems are typically trained to predict what item users will interact with next, but not why. However, offering personalized evidence for why a user might…
The Lifecycle of LLM-as-a-Judge for Large-Scale Recommendation Explanations
Emma Yanyang Kong, JJ Tan, Ishan Gupta +8
LLM-as-a-Judge, which leverages a large language model to evaluate natural language generated by another AI application or model, has become a standard, scalable approach for accel…
Multimedia Asset Personalization via Multimodal Embeddings at Netflix
Emma Yanyang Kong, Aditya Deshpande, Bowei Yan +5
Personalized promotional assets, namely artwork images and video preview clips, are critical to content discovery on Netflix. Traditional models for asset selection rely on ID-base…
Netflix Artwork Personalization via LLM Post-training
Hyunji Nam, Sejoon Oh, Emma Kong +2
Large language models (LLMs) have demonstrated success in various applications of user recommendation and personalization across e-commerce and entertainment. On many entertainment…