基于图注意力的异构图社交推荐网络

针对现有社交推荐算法忽视了用户潜在关联和项目之间的协作关系，提出了一个新的算法模型GATHGN(GAT based heterogeneous graph neural network),在该模型框架中对用户关联和项目关系统一建模。首先，挖掘用户显式社交关系、潜在关联关系和用户—项目关联关系，从而提取用户社交高阶特征和潜在兴趣高阶特征；而后，基于图注意力机制聚合上述两种高阶特征，逐层更新用户融合特征；最后，依据更新的用户融合特征与项目特征计算最终的推荐结果。在Yelp数据集和Flickr数据集上的实验结果表明，GATHGN的命中率与归一化折损累计增益较基线算法有显著提升。

Gat based heterogeneous graph neural network for social recommendation

Aiming at the problem that the existing social recommendation algorithms ignore the potential association of users and the collaborative relationship between items, this paper proposed a new algorithm model GATHGN. This model framework unified modeled of user association and item relationship. Firstly, users'' mined explicit social relations, potential association relations and user-item association relations, so as to extract users'' social high-order characteristics and potential interest high-order characteristics. Then, the above two high-level features were aggregated based on the graph attention mechanism, and the user fusion features were updated layer by layer. Finally, the final recommendation results were calculated according to the updated user fusion characteristics and project characteristics. Experimental results on Yelp and Flickr data show that the HR and NDCG of GATHGN are significantly improved compared with the baseline algorithms.