3 papers
cs.IR2026
Beyond Raw Engagement: A Counterfactual Observability Framework for Recommender Systems at Netflix
Chaoran Guo, Ding Tong, Ting-Po Lee +1
Understanding the performance of large-scale recommender systems remains an underexplored challenge, especially for content creators and model developers. The raw engagement signal…
cs.AI2026
From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation
Alireza S. Ziabari, Kat Ellis, Colleen Chan +1
Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale. While Large Language Models (LLMs) offer…
cs.AI2026
LLM Reasoning for Subjective Tasks: Failure Modes, Mitigation, and Dynamic Reasoning Routing
Juncheng Dong, Ding Tong, Ishan Gupta +1
Recommendation systems thrive on personalization, where ''correctness'' is rarely a binary truth but a matter of subjective human preference. As Large Language Models (LLMs) are de…