Title: Dual-phase temporal attention framework for energy-aware music recommendation

Authors: Long Tang

Addresses: Luoyang Normal University, Luoyang, Henan Province, China

Abstract: Personalised music recommendation systems build preference models based on users' listening history to suggest music aligned with their interests. As music streaming data volumes increase exponentially, energy consumption has become a critical concern in processing these recommendations. This paper introduces a novel energy-conscious approach to music recommendation. First, we propose a sequential preference framework that captures both enduring and recent user preferences using temporal attention networks. Second, we develop a cascaded decomposition technique to address data sparsity and imbalance challenges in large-scale music interaction data sets. Finally, we implement an energy-aware computation strategy that optimises resource utilisation during recommendation processing. Our experimental results demonstrate that the proposed framework outperforms baseline methods across multiple evaluation metrics while reducing energy consumption by up to 25%. Ablation studies confirm each component's effectiveness in enhancing recommendation quality and energy efficiency.

Keywords: music recommendation; temporal attention; energy-aware computation; sequential preference.

DOI: 10.1504/IJCAT.2025.150330

International Journal of Computer Applications in Technology, 2025 Vol.77 No.3/4, pp.263 - 273

Received: 01 Oct 2024
Accepted: 27 Aug 2025
Published online: 09 Dec 2025 *

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