MCPD: 结合预训练与去噪图卷积网络的多任务学习推荐模型

Currently, recommendation systems based on Graph Convolutional Networks (GCNs) commonly suffer from issues such as noise, low training efficiency, and inability to select appropriate loss functions for effective joint optimization. To this end, a multi task learning recommendation model MCPD is proposed, which combines pre training and denoising graph convolutional networks. The graph convolutional network focuses on the collaborative signals between high-order neighbors to generate more accurate user and item embeddings. Firstly, pre training is conducted on both the user and the project using bidirectional attention to improve the convergence speed and training time efficiency of the model. Secondly, a neighbor edge denoising autoencoder model is designed to combine traditional graph convolutional networks with attention mechanisms in the neighbor edge denoising task to identify noisy edges. The embedding is encoded and decoded using a denoising autoencoder DAE to reduce noise. Finally, select the cosine contrastive loss function with the best performance, and combine multi task learning to jointly optimize bidirectional attention aggregation pre training, neighbor edge denoising, and denoising autoencoder to ensure model recommendation accuracy. Experiments on three standard datasets showed that the Recall and NDCG metrics of the MCPD model reached 7.10, 6.00, 19.09 and 5.85, 4.82, 15.75, respectively, outperforming other baselines. In terms of recommendation accuracy, it has significant advantages compared to GCN based recommendation systems.