Time-Aware Adaptive Side Information Fusion for Sequential Recommendation
Published:Dec 30, 2025 14:15
•1 min read
•ArXiv
Analysis
This paper addresses key limitations in sequential recommendation models by proposing a novel framework, TASIF. It tackles challenges related to temporal dynamics, noise in user sequences, and computational efficiency. The proposed components, including time span partitioning, an adaptive frequency filter, and an efficient fusion layer, are designed to improve performance and efficiency. The paper's significance lies in its potential to enhance the accuracy and speed of recommendation systems by effectively incorporating side information and temporal patterns.
Key Takeaways
- •Proposes TASIF, a novel framework for sequential recommendation.
- •Addresses challenges related to temporal dynamics, noise, and computational efficiency.
- •Employs time span partitioning, adaptive frequency filtering, and a guide-not-mix fusion layer.
- •Demonstrates superior performance compared to state-of-the-art baselines.
- •Offers a publicly available source code for reproducibility.
Reference
“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; and (3) an efficient adaptive side information fusion layer, this layer employs a "guide-not-mix" architecture.”