Top-N Collaborative Filtering Recommendation Algorithm Based on Knowledge Graph Embedding

  • Ming Zhu
  • De-sheng ZhenEmail author
  • Ran Tao
  • You-qun Shi
  • Xiang-yang Feng
  • Qian Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1027)


The traditional collaborative filtering recommendation algorithm only uses the item-user rating matrix without considering the semantic information of the item itself, resulting in a problem that the recommendation accuracy is not high. This paper proposes a Top-N collaborative filtering recommendation algorithm based on knowledge graph embedding. The knowledge graph embedding is used to learn a low-dimensional vector for each entity and relationship in the knowledge graph, while maintaining the structure and semantic information of the original graph in the vector. By calculating the semantic similarity between items, the semantic information of the item itself is incorporated into the collaborative filtering recommendation. The algorithm makes up for the defect that the collaborative filtering recommendation algorithm does not consider the knowledge information of the item itself, and enhances the effect of collaborative filtering recommendation on the semantic level. The experimental results on the MovieLens dataset show that the algorithm can get higher values on precision, recall and F1 measure.


Collaborative filtering Knowledge graph embedding Recommendation algorithm Semantic similarity 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ming Zhu
    • 1
  • De-sheng Zhen
    • 1
    Email author
  • Ran Tao
    • 1
  • You-qun Shi
    • 1
  • Xiang-yang Feng
    • 1
  • Qian Wang
    • 1
  1. 1.School of Computer Science and TechnologyDonghua UniversityShanghaiChina

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