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

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...

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Bibliographic Details
Published inCyber-physical systems Vol. 10; no. 3; pp. 247 - 262
Main Authors Li, Huimin, Cheng, Yongyi, Ni, Hongjie, Zhang, Dan
Format Journal Article
LanguageEnglish
Published Taylor & Francis 02.07.2024
Subjects
attention mechanism
convolutional neural network
Deep learning
long short-term memory network
recommendation system
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Summary: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. 
ISSN:2333-5777
2333-5785
DOI:10.1080/23335777.2023.2177750