Knowledge Enhanced Recommendation

Knowledge-enhanced recommendation aims to improve the accuracy and effectiveness of recommender systems by integrating external knowledge, such as knowledge graphs, with user-item interaction data. Current research focuses on developing novel algorithms, including graph neural networks and contrastive learning methods, to effectively fuse this heterogeneous information, often leveraging large language models for knowledge extraction and representation learning. These advancements lead to more personalized and informative recommendations, with applications ranging from dietary recommendations to improved online shopping experiences, ultimately impacting both the efficiency and user satisfaction of recommender systems.