New Benchmark: Empirical Analysis of Six Audio Embedding Models for Music Recommendation
_reachsumit · x · 2026-08-10
The study systematically evaluates six representative audio encoders across three types of music recommender systems: content-based, sequential, and Semantic-ID-based generative recommenders.
It finds that most pre-trained audio representation models are optimized for objectives like classification, resulting in representation spaces not entirely suitable for recommenders. Experiments comparing residual-quantization designs show that audio-text-aligned and music-domain representation models perform better in generative recommendation scenarios.
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