Recommendation Algorithm Based on Adaptive Aggregation of Virtual Relationship Knowledge Graph
In the era of information explosion,recommendation algorithms have become an effective means to cope with information overload.In recent years,graph neural networks(GNN)have been widely applied in recommendation algo-rithms due to their powerful modeling capabilities and advantages in addressing cold start issues.A joint training framework based on deep reinforcement learning and GNN-R is proposed in this paper to address the fixed-layer and aggregation strate-gy issues in GNN-R.By employing interval experience replay and delayed reward mechanisms,the learning process of the recommendation model is optimized.Building upon this,two optimization algorithms for adaptively optimizing the aggrega-tion layers and virtual relation quantities in GNN-R are proposed,enhancing the performance of the VRKG4Rec model.Ex-perimental results demonstrate significant performance improvements of the two optimization algorithms compared to the VRKG4Rec model.
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