Recommender system based on adaptive threshold filtering GCN

Meng Qiao; Hairen Gui; Ke Tang

doi:10.1117/12.2639323

15 July 2022 Recommender system based on adaptive threshold filtering GCN

Meng Qiao, Hairen Gui, Ke Tang

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 1225806 (2022) https://doi.org/10.1117/12.2639323
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

We introduce the AT-GCN (Adaptive Threshold filtering Graph Convolutional Neural network model). AT-GCN is a recommendation model based on graph structure. Compared with the commonly used graph structure recommendation model, AT-GCN can effectively solve the problem of edge representation and information transfer, and improve the recommendation effect. In the experimental part, several groups of experiments were carried out on AT-GCN, and the above conclusions were finally verified by the experimental results.

Citation Download Citation

Meng Qiao, Hairen Gui, and Ke Tang "Recommender system based on adaptive threshold filtering GCN", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 1225806 (15 July 2022); https://doi.org/10.1117/12.2639323

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