The invention discloses a heterogeneous collaborative recommendation method and
system for multi-geometric interactive embedding and graph
convolution comparative learning, and the method comprises the steps: constructing a
knowledge graph triple and a user-article interactive graph from
original data, extracting head entities from the
knowledge graph triple and the user-article interactive graph, embedding the head entities, and mapping the head entities to a Euclidean space, a hyperbolic space and a
spherical space; after mapping results of different spaces are unified through tangent space projection, fusion of the different spaces is achieved through cross attention; after fusion is completed, a graph
attention network is used for executing attention aggregation of
message propagation, geometrical relationship coding and structure
perception on an interaction graph, and final representations of the user and the article are obtained respectively. And according to the final representations of the users and the articles, generating predicted scores of all the articles by the target user, and sorting the predicted scores to generate a recommendation
list to finish article recommendation.