Abstract

The burgeoning of e-commerce and online platforms has led to an explosion in data volume and diversity of user preferences, making effective recommendation systems crucial for personalizing user experiences. While collaborative filtering algorithms are traditionally favoured for their ability to leverage user-item interactions, they grapple with data sparsity and noise challenges. To tackle these challenges, Various approaches have emerged in recent years to tackle these challenges. Recent strides in deep learning, particularly autoencoders and neural networks, have shown promise in addressing these issues. However, limitations persist, such as suboptimal feature extraction and the underutilization of combined nonlinear and linear latent features in traditional autoencoders, as well as the overlooked impact of active users in recommendations. Addressing these research gaps, this study introduces a novel recommendation algorithm that synergizes a deep denoising autoencoder with an attention mechanism, aiming to refine recommendation performance by mitigating data sparsity and enhancing feature extraction. This fusion approach innovatively combines nonlinear and linear latent features and incorporates a neural attention mechanism, significantly improving the precision and personalization of recommendations. Ultimately, the proposed algorithm's effectiveness is assessed and benchmarked against state-of-the-art approaches, demonstrating its potential to revolutionize recommendation systems by offering more accurate and user-tailored suggestions.

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Cite this article

@article{cai-6982,
  author  = {Zixuan Han and Leilei Shi and Qiang Sun and Xiuliang Huang and Bing Lei and Lu Liu and Yao Lu},
  title   = {Collaborative Filtering Algorithm Based on Deep Denoising Auto-Encoder and Attention Mechanism},
  journal = {Computing and Informatics},
  volume  = {44},
  number  = {1},
  pages   = {176--201},
  year    = {2025},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2025_1_176}
}
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