A DE-LGCA personalized recommendation algorithm study

Qingda Zhang; Zhiguo Shi

doi:10.1117/12.2678846

25 May 2023 A DE-LGCA personalized recommendation algorithm study

Qingda Zhang, Zhiguo Shi

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 1271208 (2023) https://doi.org/10.1117/12.2678846
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

A graph convolutional neural network is a special type of neural network that GCN can use to extract features from graphs and use these features for classification or regression. The current lightweight graph convolutional neural network in the recommendation field only considers the case of fixed neighborhood weights, with the increase in the number of training layers of the network recommendation effect will rapidly decline resulting in the problem of oversmoothing and overfitting. In this paper, in order to solve the above problem, this paper will introduce a DE-LGCA personalized recommendation algorithm that uses a random wandering strategy for data enhancement based on a lightweight graph convolutional neural, while introducing an attention mechanism to increase the node neighborhood weights, and show how to use this algorithm to achieve personalized recommendations. Experiments were conducted on three publicly available datasets, and three performance metrics, Precision, Recall, and Normalized Discounted Cumulative Gain (NDCG), were used to evaluate the performance of DE-LGCA, which not only outperformed the comparison method in terms of performance metrics, but also improved the efficiency of the algorithm.

Citation Download Citation

Qingda Zhang and Zhiguo Shi "A DE-LGCA personalized recommendation algorithm study", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 1271208 (25 May 2023); https://doi.org/10.1117/12.2678846

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