Research of item recommendation algorithm based on graph neural network and deep learning

Jianfeng Zheng; Yinshan Jia

doi:10.1117/12.2681539

1 June 2023 Research of item recommendation algorithm based on graph neural network and deep learning

Jianfeng Zheng, Yinshan Jia

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127182I (2023) https://doi.org/10.1117/12.2681539
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

Abstract

The recommendation algorithm based on graph neural network has advantage than the traditional recommendation algorithm that it has the power to extract the association relationship between users and items, which makes it become a research hotspot. However, most of the item recommendation algorithm models based on graph neural network are too focused on the interaction information between users and items, ignore the user preferences and item attributes contained in the review data of users and items. In order to solve this problem, this thesis proposed a new model. This model used the graph neural network to extract high-order feature from the interaction information between items and users, and deep learning method to get the general feature from review data. Then it got the final feature of items and users by carrying out feature fusion, which used the attention mechanism. The thesis conducted comparative experiments and ablation experiments on four sets of data sets and the evaluation indexes include the normalized discounted cumulative gain and recall rate. Experiments results show that this model is more effective than the existing algorithms, the feature fusion has positive effect on improving the recommendation accuracy.

Citation Download Citation

Jianfeng Zheng and Yinshan Jia "Research of item recommendation algorithm based on graph neural network and deep learning", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127182I (1 June 2023); https://doi.org/10.1117/12.2681539

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