PEVGraphRec: a PEV method-based graph neural networks for social recommendations

Dong Liao; Haizheng Yu

doi:10.1117/12.2656825

13 January 2023 PEVGraphRec: a PEV method-based graph neural networks for social recommendations

Dong Liao, Haizheng Yu

Proceedings Volume 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022); 125101O (2023) https://doi.org/10.1117/12.2656825
Event: International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 2022, Qingdao, China

Abstract

Most existing recommendation systems assume that items are independent and identically distributed. These methods only leverage user-item interactions as model input, while neglecting the relationship between items, leading to limited performance. To address this issue, this paper proposes the PEVGraphRec, which provides recommendations by utilizing Proximity-Effect-Value (PEV) similarity measurement to generate an item-item implicit network and employing graph neural network with attention mechanisms to intelligently aggregate the user-user connections, the user-item interactions, and the item-item relations. Specially, compared with traditional similarity measurement, the PEV method can reduce the negative effects of cold start. Experiments on three real-world datasets verify the interpretability of our model by examining the contribution of each component. The results show that the PEVGraphRec is robust and superior to other baseline methods.

Citation Download Citation

Dong Liao and Haizheng Yu "PEVGraphRec: a PEV method-based graph neural networks for social recommendations", Proc. SPIE 12510, International Conference on Statistics, Data Science, and Computational Intelligence (CSDSCI 2022), 125101O (13 January 2023); https://doi.org/10.1117/12.2656825

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