A Neig-DoubleRipple recommendation model based on fused neighbourhood attention and aggregation

HongWei Chen; QiGang Li

doi:10.1117/12.3004865

9 October 2023 A Neig-DoubleRipple recommendation model based on fused neighbourhood attention and aggregation

HongWei Chen, QiGang Li

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 1279126 (2023) https://doi.org/10.1117/12.3004865
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In recommendation systems, the use of information from knowledge graphs to enhance the effectiveness of recommendations has become a hot research topic. Currently, the RippleNet model has become a relatively effective knowledge graph-based recommendation model. However, the RippleNet model only uses the information between the user to be predicted and the item, and does not take into account the information contained in the neighbouring entities around the item to be predicted. To solve this problem, this paper proposes a new model based on RippleNet, Neig-DoubleRipple, which augments the original model by adding a neighbourhood attention mechanism and a neighbourhood entity aggregation method similar to the KGAT model. The experimental results show that the new model achieves 92.52% in auc and 85.11% in acc, indicating that the method proposed in this paper can significantly improve the performance of the RippleNet model.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

HongWei Chen and QiGang Li "A Neig-DoubleRipple recommendation model based on fused neighbourhood attention and aggregation", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 1279126 (9 October 2023); https://doi.org/10.1117/12.3004865

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