华南理工大学学报（自然科学版）2026，Vol.54Issue(2)：52-61,10.DOI:10.12141/j.issn.1000-565X.250179
基于社交扩散和自适应负采样的推荐算法
Recommendation Algorithm Based on Social Diffusion and Adaptive Negative Sampling
摘要
Abstract
Social recommendation algorithms based on Graph Neural Network(GNN)leverage social networks to improve recommendation performance.However,most existing methods directly integrate the raw social graph into the recommendation system,which often introduces noise as they overlook the presence of non-homophilous social connections.Furthermore,prevailing negative sampling strategies typically select negative samples with a fixed level of hardness,which is prone to generating false negatives and consequently limits the model's ability to effec-tively discriminate between user preferences.To address these issues,this paper proposed a novel recommendation algorithm based on social diffusion and adaptive negative sampling.First,forward diffusion and interest-guided denoising were performed on the social network to derive user representations that reflect homophilic social relations.Subsequently,a multi-view representation alignment approach was employed to maximize the mutual information among user representations from the denoised social graph,the original social graph,and the user-item interaction graph,thereby enhancing the quality of user embeddings.Finally,negative samples of adaptive hardness were selected based on the predicted scores of positive samples,enabling dynamic calibration of the similarity boundary between positive and negative pairs to improve overall model performance.Extensive experimental results demon-strate that the proposed algorithm significantly outperforms state-of-the-art recommendation baselines.On the Douban dataset,it improves recall and NDCG by 11.99%and 10.54%,respectively;on Epinions,by 15.62%and 11.14%;and on Yelp,by 13.80%and 14.90%.These results validate its effectiveness in alleviating social noise and enhancing the differentiation between positive and negative samples.关键词
推荐算法/社交网络/图神经网络/扩散模型/对比学习/负采样Key words
recommendation algorithm/social network/graph neural network/diffusion model/contrastive learning/negative sampling分类
信息技术与安全科学引用本文复制引用
蔡晓东,李婷,苏一峰..基于社交扩散和自适应负采样的推荐算法[J].华南理工大学学报（自然科学版）,2026,54(2):52-61,10.基金项目
国家自然科学基金项目(62177012)Supported by the National Natural Science Foundation of China(62177012) （62177012）
