Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge
This addresses the problem of costly and biased evaluation in podcast recommender systems for developers and researchers, though it is incremental as it adapts existing LLM-as-a-judge methods to a specific domain.
The paper tackles the challenge of evaluating personalized podcast recommendations by introducing a profile-aware LLM-as-a-judge framework, which matched human judgments with high fidelity in a study with 47 participants and outperformed or matched a variant using raw listening histories.
Evaluating personalized recommendations remains a central challenge, especially in long-form audio domains like podcasts, where traditional offline metrics suffer from exposure bias and online methods such as A/B testing are costly and operationally constrained. In this paper, we propose a novel framework that leverages Large Language Models (LLMs) as offline judges to assess the quality of podcast recommendations in a scalable and interpretable manner. Our two-stage profile-aware approach first constructs natural-language user profiles distilled from 90 days of listening history. These profiles summarize both topical interests and behavioral patterns, serving as compact, interpretable representations of user preferences. Rather than prompting the LLM with raw data, we use these profiles to provide high-level, semantically rich context-enabling the LLM to reason more effectively about alignment between a user's interests and recommended episodes. This reduces input complexity and improves interpretability. The LLM is then prompted to deliver fine-grained pointwise and pairwise judgments based on the profile-episode match. In a controlled study with 47 participants, our profile-aware judge matched human judgments with high fidelity and outperformed or matched a variant using raw listening histories. The framework enables efficient, profile-aware evaluation for iterative testing and model selection in recommender systems.