Balancing Accuracy and Novelty with Sub-Item Popularity

• URL: http://arxiv.org/abs/2508.05198v1
• Date: Thu, 07 Aug 2025 09:33:32 GMT
• Title: Balancing Accuracy and Novelty with Sub-Item Popularity
• Authors: Chiara Mallamaci, Aleksandr Vladimirovich Petrov, Alberto Carlo Maria Mancino, Vito Walter Anelli, Tommaso Di Noia, Craig Macdonald,
• Abstract summary: We propose a novel integration of sub-ID-level personalised popularity within the RecJPQ framework.<n>Our method consistently outperforms item-level PPS by achieving significantly higher personalised novelty without compromising recommendation accuracy.
• Score: 54.56622169534604
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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