Why one size doesn't fit all: Improving music discovery and familiar listening with specialized models

Collaborative filtering is a foundational component of music recommender systems, powering a variety of recommendation tasks from retrieving the most relevant tracks, albums, artists, and podcasts for a given user to more nuanced objectives such as content discovery, familiar listening, and new release recommendation. To enable scalable, low-latency inference, content-retrieval models compute latent user and item representations. These embeddings can be efficiently queried using approximate nearest-neighbor indices. A fundamental challenge in music recommendation systems is the catalog scale, with music streaming platforms commonly offering more than 100 million tracks. Training item embeddings at this scale presents significant operational challenges, leading to the prevalent industry practice of training a single, general-purpose content-retrieval model and adapting it to specific tasks through post-hoc filtering. For instance, discovery tasks apply filtering to exclude previously consumed content, while new release recommendations filter by recency. We challenge this practice by quantifying substantial performance improvements of task-specific models over the standard approach of using a single general-purpose model with post-hoc filtering, with gains of up to 194.6% in NDCG@100 for discovery tasks and up to 122.4% for fine-tuned models. To mitigate the training costs that motivate the industry practice in the first place, we explore an intermediate approach: fine-tuning a foundational content-retrieval model on specific recommendation tasks. While fine-tuned models do not achieve the same performance as fully task-specific models, they consistently outperform the single embedding model approach at comparable training costs.