Abstract: A neighboring user node of a user node is selected by using an attention model, to determine a selective adjacency matrix of a relation network based on the selected neighboring user node. Then, a neighboring node representation is propagated to a corresponding user node based on a selective adjacency matrix by using a graph neural network, to obtain a user aggregation representation. A tapping behavior between a user and an object is fitted based on a similarity between the user aggregation representation and an object representation, to construct a prediction loss based on a difference between the tapping behavior and an existing tapping behavior, and update the attention model. The trained attention model can select a more reliable neighboring user.