{"ID":2885117,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.05198","arxiv_id":"2508.05198","title":"Balancing Accuracy and Novelty with Sub-Item Popularity","abstract":"In the realm of music recommendation, sequential recommenders have shown promise in capturing the dynamic nature of music consumption. A key characteristic of this domain is repetitive listening, where users frequently replay familiar tracks. To capture these repetition patterns, recent research has introduced Personalised Popularity Scores (PPS), which quantify user-specific preferences based on historical frequency. While PPS enhances relevance in recommendation, it often reinforces already-known content, limiting the system's ability to surface novel or serendipitous items - key elements for fostering long-term user engagement and satisfaction. To address this limitation, we build upon RecJPQ, a Transformer-based framework initially developed to improve scalability in large-item catalogues through sub-item decomposition. We repurpose RecJPQ's sub-item architecture to model personalised popularity at a finer granularity. This allows us to capture shared repetition patterns across sub-embeddings - latent structures not accessible through item-level popularity alone. We propose a novel integration of sub-ID-level personalised popularity within the RecJPQ framework, enabling explicit control over the trade-off between accuracy and personalised novelty. Our sub-ID-level PPS method (sPPS) consistently outperforms item-level PPS by achieving significantly higher personalised novelty without compromising recommendation accuracy. Code and experiments are publicly available at https://github.com/sisinflab/Sub-id-Popularity.","short_abstract":"In the realm of music recommendation, sequential recommenders have shown promise in capturing the dynamic nature of music consumption. A key characteristic of this domain is repetitive listening, where users frequently replay familiar tracks. To capture these repetition patterns, recent research has introduced Personal...","url_abs":"https://arxiv.org/abs/2508.05198","url_pdf":"https://arxiv.org/pdf/2508.05198v1","authors":"[\"Chiara Mallamaci\",\"Aleksandr Vladimirovich Petrov\",\"Alberto Carlo Maria Mancino\",\"Vito Walter Anelli\",\"Tommaso Di Noia\",\"Craig Macdonald\"]","published":"2025-08-07T09:33:32Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false,"code_links":[{"ID":611150,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2885117,"paper_url":"https://arxiv.org/abs/2508.05198","paper_title":"Balancing Accuracy and Novelty with Sub-Item Popularity","repo_url":"https://github.com/sisinflab/Sub-id-Popularity","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}