Abstract: Personalized recommendation systems are widely used in reducing information overload, providing personalized services, and assisting users in decision-making. Link prediction is one of the important methods of personalized recommendation. Traditional heuristic link prediction methods only consider the graph structure characteristics of the network, and lack the application of explicit and implicit feature information, and most methods are based on undirected and unweighted networks. Aiming at the shortcomings of traditional link prediction methods, this paper proposes a link prediction method AFP based on the collective attention flow network and the R-GCN method. The different edge directions between the two nodes in the attention flow network are abstracted into two types of edge relations. The attention mechanism is introduced to learn the node attributes and edge attributes in the network, and the network's graph structure characteristics, implicit characteristics and explicit features are comprehensively considered. The scoring function is used to get the probability of the establishment of the triple, and the link prediction problem is transformed into a two-category problem, thus predicting the possibility that the edges between nodes belong to a certain type of relationship. Experiments show that, compared with 6 benchmark models such as GCN and GAT, this method improves the accuracy, precision, recall and other evaluation indicators.

Key words: link prediction, directed weighted graph, attention mechanism, R-GCN