Guang Wang
ABSTRACT
Although Few methods of deep learning have been proposed to deal with graphs that are dynamic in nature. Most of the existing recommendation systems based on social trust ignore the heterogeneous trust relationship between users and the heterogeneous interaction between users and products. And the appeal of a project can change over time, just as the dynamic interests of users are rarely considered, so is the relevance between projects. In order to overcome these limitations, a graph attention neural network Social recommendation (GNN-DR) model based on dynamic representation is proposed. Firstly, it considers the dynamic representation of users and items, and combines the influence of their relationship to model the short-term dynamic and long-term static interactive representation of item attractiveness. Second, the architecture systematically models user-user social diagrams and user-project diagrams, integrating heterogeneous trust and interactions. Finally, the attention mechanism is used to learn the potential factors of users and projects. Experiments on two real recommendation system datasets verify the validity of GNN-DR.
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Index Terms
- Graph attention neural network social recommendation based on dynamic representation
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