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

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. 

KEYWORDS:

• Deep learning
• convolutional neural network
• attention mechanism
• long short-term memory network
• recommendation system

Acknowledgments

This work was partially supported by the National Key R&D Funding under Grant No. 2018YFB1403702, the Zhejiang Provincial Natural Science Foundation of China for Distinguished Young Scholars under Grant No. LR22F030003, the National Natural Science Foundation of China under Grant No. 61873237, the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant No. RF-A2019003 and Major Project of Science and Technology Innovation in Ningbo City under Grant No. 2019B1003.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the National Key R&D Funding [2018YFB1403702].