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Dual-path recommendation algorithm based on CNN and attention-enhanced LSTM

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  • Huimin Li
  • Yongyi Cheng
  • Hongjie Ni
  • Dan Zhang

Abstract

To recommend useful information to users more efficiently, this paper proposes a dual-path recommendation algorithm which combines multilayer Convolutional Neural Network (CNN) and attention-enhanced long short-term memory network (Attention-LSTM). Firstly, the matrix factorisation technique is used for learning the long-term preferences of users. Secondly, a dual-path network based on CNN and LSTM is constructed to perform feature extraction on the rating matrix. The dual-path network can learn the long-term preferences of users while capturing their dynamic preferences in changing preferences. The algorithm is tested on the public dataset MovieLens-1M, and the MAE value reflects the accuracy of the algorithm.

Suggested Citation

  • Huimin Li & Yongyi Cheng & Hongjie Ni & Dan Zhang, 2024. "Dual-path recommendation algorithm based on CNN and attention-enhanced LSTM," Cyber-Physical Systems, Taylor & Francis Journals, vol. 10(3), pages 247-262, July.
    • Handle: RePEc:taf:tcybxx:v:10:y:2024:i:3:p:247-262
    DOI: 10.1080/23335777.2023.2177750
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