Dynamic Knowledge Selector and Evaluator for recommendation with Knowledge Graph
This work addresses accuracy issues in recommendation systems for users, but it is incremental as it builds on existing knowledge graph and graph network methods.
The paper tackles the problem of noisy entities and sparse labels in knowledge graph-based recommendation systems by proposing a dynamic knowledge selector and evaluator guided by collaborative signals, resulting in improved accuracy over state-of-the-art models on three public datasets.
In recent years recommendation systems typically employ the edge information provided by knowledge graphs combined with the advantages of high-order connectivity of graph networks in the recommendation field. However, this method is limited by the sparsity of labels, cannot learn the graph structure well, and a large number of noisy entities in the knowledge graph will affect the accuracy of the recommendation results. In order to alleviate the above problems, we propose a dynamic knowledge-selecting and evaluating method guided by collaborative signals to distill information in the knowledge graph. Specifically, we use a Chain Route Evaluator to evaluate the contributions of different neighborhoods for the recommendation task and employ a Knowledge Selector strategy to filter the less informative knowledge before evaluating. We conduct baseline model comparison and experimental ablation evaluations on three public datasets. The experiments demonstrate that our proposed model outperforms current state-of-the-art baseline models, and each modules effectiveness in our model is demonstrated through ablation experiments.