The invention provides a recommendation method for aggregating a
knowledge graph neural network and adaptive attention, and the method comprises the following steps: S1, taking a
knowledge graph triple of a user, a relationship and an entity as an input, and distributing initial embedding representations, namely a user embedding representation, a relationship embedding representation and an entity embedding representation, for the input; S2, using an inner product to represent the importance degree of the relationship to the user; converting the heterogeneous
knowledge graph into a weighted graph, then selecting neighbor target nodes, and training domain embedding representation of the neighbor target nodes; feeding the initial entity embedding representation into a graph neural network for training and generating a new entity embedding representation; performing
polymerization to obtain a final article embedding expression; and S3, taking an inner product of the user embedded representation and the final article embedded representation as a final
prediction score, and recommending the article corresponding to the highest
score to the user. According to the method, the limitation problem that a
matrix decomposition algorithm only utilizes interaction between a user and an article is effectively solved, and the neighbor nodes are considered when the vector representation of the neighborhood of the target node is aggregated.