Knowledge filter contrastive learning for recommendation

Knowledge graph-based recommender systems integrate user–item interactions with knowledge graph information through Graph Neural Networks (GNNs), demonstrating their effectiveness in addressing data sparsity and cold start issues. However, existing knowledge graph has more invalid connections; these noise connections are amplified during message aggregation in GNNs. To alleviate this problem, this paper proposes a contrastive learning filtering method based on multi-view generation. Traditional denoising methods mainly use edge perturbation or graph diffusion to randomly add or remove data, which are highly uncertain and often destroy the semantic information of the original data. We proposed the multi-view generation-based contrastive learning method can perform multiple different samples on the original data to create contrasting views. This ensures the semantic completeness of the original data while also providing rich semantic samples for contrastive learning. Specifically, the method first performs multiple rounds of data sampling from the user–item interaction graph and the knowledge graph to generate multiple views of items. Subsequently, knowledge embedding techniques are used to vectorize entities and relationships within these views. Finally, a contrastive task uses a designed loss to share semantic info among items, controlling node connections with item similarity. Through this method, the information of neighboring nodes (entities or relationships) can be propagated, the importance of neighboring nodes can be distinguished, and the recommendation result can be more precise. Extensive experiments on three benchmark datasets demonstrate that our proposed multi-view contrastive learning filtering approach significantly enhances performance in knowledge graph-based recommendation tasks.