Aiming at the over-smoothing and noise problems in the embedding representation in the message passing process of graph convolution based on graph neural network recommendation， a Recommendation model combining Self-features and Contrastive Learning （SfCLRec） was proposed. The model was trained using a pre-training-formal training architecture. Firstly， the embedding representations of users and items were pre-trained to maintain the feature uniqueness of the nodes themselves by fusing the node self-features and a hierarchical contrastive learning task was introduced to mitigate the noisy information from the higher-order neighboring nodes. Then， the collaborative graph adjacency matrix was reconstructed according to the scoring mechanism in the formal training stage. Finally， the predicted score was obtained based on the final embedding. Compared with existing graph neural network recommendation models such as LightGCN and Simple Graph Contrastive Learning （SimGCL）， SfCLRec achieves the better recall and NDCG （Normalized Discounted Cumulative Gain） in three public datasets ML-latest-small， Last.FM and Yelp， validating the effectiveness of SfCLRec.

Aiming at the problem of noise arising from user’s unexpected interactions in practical recommendation scenarios and the challenge of capturing short-term demand biases due to the dispersed attention in self-attention mechanism， a model namely FTARec （sequential Recommendation based on hierarchical Filter and Temporal convolution enhanced self-Attention network） was proposed. Firstly， hierarchical filter was used to filter noise in the original data. Then， user embeddings were obtained by combining temporal convolution enhanced self-attention networks with decoupled hybrid location encoding. The deficiencies in modeling short-term dependencies among items were supplemented by enhancing the self-attention network with temporal convolution in this process. Finally， contrastive learning was incorporated to refine user embeddings and predictions were made based on the final user embeddings. Compared to existing sequential recommendation models such as the Self-Attentive Sequential Recommendation （SASRec） and the Filter-enhanced Multi-Layer Perceptron approach for sequential Recommendation （FMLP-Rec）， FTARec achieves higher Hit Rate （HR） and Normalized Discounted Cumulative Gain （NDCG） on three publicly available datasets： Beauty， Clothing， and Sports. Compared with the suboptimal DuoRec， FTARec has the HR@10 increased by 7.91%， 13.27%， 12.84%， and the NDCG@10 increased by 5.52%， 8.33%， 9.88%， respectively， verifying the effectiveness of the proposed model.