A social recommendation based on GCN improved by social sampling

Yang GuangZe; Wang Yong

doi:10.1117/12.2640519

15 July 2022 A social recommendation based on GCN improved by social sampling

Yang GuangZe, Wang Yong

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 1225807 (2022) https://doi.org/10.1117/12.2640519
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

To avoid data overload, recommendation systems have been created. Due to the difficulty of data collection, the recommendation system faces a cold start and needs to introduce auxiliary information. In this paper, we use social recommendation to solve the cold start, and we adopt a graph convolutional neural network to aggregate high-order neighbors and sample the neighbors for the auxiliary recommendation system. Ultimately our model achieves impressive results on classical datasets. Compared to the baselines, we achieved a higher accuracy rate.

Citation Download Citation

Yang GuangZe and Wang Yong "A social recommendation based on GCN improved by social sampling", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 1225807 (15 July 2022); https://doi.org/10.1117/12.2640519

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