WeMap Recommendation by Fusion of Knowledge Graph and Collaborative Filtering

Based on sparse matrix, traditional collaborative filtering techniques usually have a low recommendation accuracy, since they cannot capture the correlations between auxiliary information from the sparse data. To fill the gap, this paper proposes a Knowledge Graph embedding Collaborative Filtering (KGCF) model to improve recommendation accuracy. In this model, the knowledge graph is introduced as auxiliary information, taking advantage of its multi- source structured data to alleviate the problem of data sparsity. By combining the semantic information of the knowledge graph and the preference information of collaborative filtering, the KGCF model can mine the interaction between users and WeMap to implement customized recommendations. Specifically, the knowledge graph and collaborative filtering algorithm are first combined to train the model on WeMap datasets. Secondly, the similarity matrix between users is calculated using the Pearson correlation coefficient, and the cryptic meaning matrix is decomposed through a sparse scoring matrix. In addition, the preference information of users and place names of WeMap is obtained using Baseline. Then, the semantic information of each object is transformed into a low dimension vector by the knowledge graph, and the similarity matrix between WeMap place names is calculated by cosine similarity. Finally, the users and place names of the WeMap are integrated into a recommendation result set. The experiments on WeMap datasets prove that the proposed KGCF model can effectively solve data sparsity and accurately recommend WeMaps of interest for users. © 2024 Science Press. All rights reserved.