Time-Aware Adaptive Side Information Fusion for Sequential Recommendation

• URL: http://arxiv.org/abs/2512.24246v1
• Date: Tue, 30 Dec 2025 14:15:06 GMT
• Title: Time-Aware Adaptive Side Information Fusion for Sequential Recommendation
• Authors: Jie Luo, Wenyu Zhang, Xinming Zhang, Yuan Fang,
• Abstract summary: We propose the Time-Aware Adaptive Side Information Fusion framework.<n> TASIF integrates a simple, plug-and-play time span partitioning mechanism to capture global temporal patterns.<n>Experiments on four public datasets demonstrate that TASIF significantly outperforms state-of-the-art baselines.
• Score: 16.495823510290744
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Incorporating item-side information, such as category and brand, into sequential recommendation is a well-established and effective approach for improving performance. However, despite significant advancements, current models are generally limited by three key challenges: they often overlook the fine-grained temporal dynamics inherent in timestamps, exhibit vulnerability to noise in user interaction sequences, and rely on computationally expensive fusion architectures. To systematically address these challenges, we propose the Time-Aware Adaptive Side Information Fusion (TASIF) framework. TASIF integrates three synergistic components: (1) a simple, plug-and-play time span partitioning mechanism to capture global temporal patterns; (2) an adaptive frequency filter that leverages a learnable gate to denoise feature sequences adaptively, thereby providing higher-quality inputs for subsequent fusion modules; and (3) an efficient adaptive side information fusion layer, this layer employs a "guide-not-mix" architecture, where attributes guide the attention mechanism without being mixed into the content-representing item embeddings, ensuring deep interaction while ensuring computational efficiency. Extensive experiments on four public datasets demonstrate that TASIF significantly outperforms state-of-the-art baselines while maintaining excellent efficiency in training. Our source code is available at https://github.com/jluo00/TASIF.

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