Cold-Start Recommendation based on Knowledge Graph and Meta-Learning under Positive and Negative sampling

This paper introduces a novel recommendation framework called CRKM (Cold-start Recommendation based on Knowledge Graph and Meta-learning), aimed at enhancing cold-start recommendation performance by addressing the issue of limited user interaction data through the fusion of positive and negative samples. In contrast to other cold-start frameworks, CRKM is divided into three distinct components: the negative sampler, the knowledge graph-based model architecture, and the meta-learner. The negative sampler designed in this paper leverages knowledge graphs and popularity information to sample negative labels from items without prior user interaction, thereby mitigating the sparsity of cold-start training data. On the other hand, the knowledge graph-based model architecture is responsible for incorporating the nodes and relationships of the knowledge graph into positive and negative samples, using a graph neural network to more effectively learn user and item fusion representations and enhance predictive performance. Finally, the meta-learner performs efficient model initialization parameter updates. We conducted extensive experiments on real-world datasets for cold-start user and item recommendations. CRKM demonstrated notable performance advantages in terms of recall and NDCG when compared to the state-of-the-art methods, thereby validating the rationality and effectiveness of the proposed approach. The source code listing is publicly available at https://gitee.com/kyle-liao/crkm.