Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation

• URL: http://arxiv.org/abs/2504.13614v1
• Date: Fri, 18 Apr 2025 10:42:16 GMT
• Title: Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
• Authors: Zahra Akhlaghi, Mostafa Haghir Chehreghani,
• Abstract summary: We introduce the Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation (ALDA4Rec) method.<n>ALDA4Rec is a novel model that constructs an item-item graph, filters noise through community detection, and enriches user-item interactions.<n>Experiments conducted on four real-world datasets demonstrate that ALDA4Rec outperforms state-of-the-art baselines.
• Score: 1.0128808054306186
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of the internet has made personalized recommendation systems indispensable. Graph-based sequential recommendation systems, powered by Graph Neural Networks (GNNs), effectively capture complex user-item interactions but often face challenges such as noise and static representations. In this paper, we introduce the Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation (ALDA4Rec) method, a novel model that constructs an item-item graph, filters noise through community detection, and enriches user-item interactions. Graph Convolutional Networks (GCNs) are then employed to learn short-term representations, while averaging, GRUs, and attention mechanisms are utilized to model long-term embeddings. An MLP-based adaptive weighting strategy is further incorporated to dynamically optimize long-term user preferences. Experiments conducted on four real-world datasets demonstrate that ALDA4Rec outperforms state-of-the-art baselines, delivering notable improvements in both accuracy and robustness. The source code is available at https://github.com/zahraakhlaghi/ALDA4Rec.

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