Evaluating Podcast Recommendations with Profile-Aware LLM-as-a-Judge

TL;DR

This paper introduces a scalable, interpretable framework using Large Language Models as offline judges to evaluate personalized podcast recommendations based on user profiles derived from listening history.

Contribution

It presents a novel profile-aware approach that constructs natural-language user profiles to enable LLMs to assess recommendation quality effectively.

Findings

LLMs as judges match human judgments with high fidelity.

Profile-based evaluation outperforms raw data-based methods.

Framework supports efficient iterative testing in recommender systems.

Abstract

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.