Social recommendation based on contrastive learning of hypergraph convolution

Recommendation systems often use social relationships to improve recommendation quality in the face of sparse data. However, in reality, user interactions can be very complex, user relationships may be high-order, and the contribution of each neighbor may be different. Therefore, traditional social relationship methods cannot fully explore user interactions. Additionally, data sparsity can lead to poor model robustness, making it susceptible to noisy data. To address these issues, this paper proposes a multi-channel hypergraph convolutional network model based on contrastive learning, aiming to enhance social recommendation through high-order user relationships and contrastive learning. The model includes an embedding layer, a propagation layer, and a contrastive learning layer. For user embedding, each channel of the propagation layer learns high-order embedding information through hypergraph convolution. For item embedding, the neighbor-aware attention coefficient is used to mine the implicit correlations of items. In addition, the contrastive learning layer adds randomly uniform noise to the embedding to perform graph augmentation at the representation level, using contrastive learning to obtain more uniformly distributed embedding representations for score prediction. Experimental results on two real datasets, Douban and Yelp, show that the proposed model improves Predict, Recall and NDCG by 1.42-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}2.11%, 1.30-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}2.30% and 1.89-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$-$$\end{document}2.74%, respectively.