基于图注意力的异构图社交推荐网络.

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 algorithm. [ABSTRACT FROM AUTHOR]