4.6 Article

Research of Personalized Recommendation Technology Based on Knowledge Graphs

Journal

APPLIED SCIENCES-BASEL
Volume 11, Issue 15, Pages -

Publisher

MDPI
DOI: 10.3390/app11157104

Keywords

knowledge graph; representation learning; personalized recommendation; Bayesian personalized ranking

Funding

  1. National Natural Science Foundation of China [91846303]
  2. Beijing Municipal Natural Science Foundation [4212043]

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This research focuses on personalized recommendation based on knowledge graphs, including constructing knowledge graphs, improving the TransE algorithm, and combining ranking learning and neural networks to build two recommendation models. Experimental results demonstrate that these models effectively enhance recommendation accuracy and recall.
Nowadays, personalized recommendation based on knowledge graphs has become a hot spot for researchers due to its good recommendation effect. In this paper, we researched personalized recommendation based on knowledge graphs. First of all, we study the knowledge graphs' construction method and complete the construction of the movie knowledge graphs. Furthermore, we use Neo4j graph database to store the movie data and vividly display it. Then, the classical translation model TransE algorithm in knowledge graph representation learning technology is studied in this paper, and we improved the algorithm through a cross-training method by using the information of the neighboring feature structures of the entities in the knowledge graph. Furthermore, the negative sampling process of TransE algorithm is improved. The experimental results show that the improved TransE model can more accurately vectorize entities and relations. Finally, this paper constructs a recommendation model by combining knowledge graphs with ranking learning and neural network. We propose the Bayesian personalized recommendation model based on knowledge graphs (KG-BPR) and the neural network recommendation model based on knowledge graphs (KG-NN). The semantic information of entities and relations in knowledge graphs is embedded into vector space by using improved TransE method, and we compare the results. The item entity vectors containing external knowledge information are integrated into the BPR model and neural network, respectively, which make up for the lack of knowledge information of the item itself. Finally, the experimental analysis is carried out on MovieLens-1M data set. The experimental results show that the two recommendation models proposed in this paper can effectively improve the accuracy, recall, F1 value and MAP value of recommendation.

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