From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation
Traditional offline recommendation evaluation relies heavily on complex, manually maintained feature pipelines that are difficult to scale. While Large Language Models (LLMs) offer a promising alternative by predicting user engagement directly from raw text logs, empirical analysis in this study identifies a critical failure mode termed bidirectional rationalization. In a zero-shot setting, LLMs are found to convincingly argue for both positive and negative user engagement outcomes on the exact
Record details
Published: 11 August 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 13 August 2026
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ethics.ai (11 August 2026), “From Prompting to Behavioral Alignment: Personalized LLM Judges for Recommendation Evaluation,” evidence record 18805, https://ethics.ai/record/18805 (originally published by arXiv).
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