Recommendation model based on knowledge graphs and semantic alignment

Most current recommendation models based on knowledge graphs use graph attention mechanisms to perform semantic aggregation of nodes and their neighbors, but this approach can lead to the loss of consistency information among neighboring nodes. Moreover, many recommendation models incorporate contrastive learning to suppress noise in knowledge graphs, often neglecting to enhance model robustness through parameter updates. Therefore, we proposed a recommendation model called FITAALIGN that integrates knowledge graphs with semantic alignment. Firstly, it is argued that the embedded representation of a node aggregated with its neighboring nodes in the knowledge graphs should possess similar semantic representations, which is achieved through similarity calculations to ensure embedded similarity of node representations. Secondly, conditional alignment is used to overcome the loss of node consistency information caused by encoding user-item graphs, thereby obtaining high-quality node semantic representations. Finally, to mitigate the impact of noise interference in the knowledge graphs, adversarial noise is firstly introduced into the model's network parameters, followed by adversarial training to strengthen the robustness of the model. Experiment results on two public datasets validate that our model outperforms some advanced methods. Respectively, on the Recall@20, there was an average increase of 54.99% and 14.57%; on the NDCG@20, the increases were 60.22% and 23.76%; and on the HR@20, the increases were 42.94% and 10.70%.