Semantic Recommendation Model via Fusing Knowledge Graph and Formal Concept Analysis

Abstract

The core idea of semantic recommendation is to incorporate semantic knowledge into the recommendation process. The semantic recommendation algorithm, based on knowledge graph, ignores the deep implicit semantics of the evaluation data. The semantic recommendation algorithm based on the deep matrix decomposition model is limited to the implicit semantics of the evaluation data. The semantic recommendation algorithm based on the collaborative filtering algorithm performs only the selection of the nearest neighbors of the user or the item unilaterally and ignores the influence of other aspects, which naturally leads to a decrease in the recommendation accuracy. To solve the above problems, this paper introduces Formal Concept Analysis (FCA) based on collaborative filtering. Using the property that the formal concept in FCA can cluster objects (users) and attributes (items) simultaneously, we propose a semantic recommendation algorithm (SRKGFCA) based on the knowledge graph and formal concept analysis to solve the problem of ignoring user or item factors. Finally, the proposed semantic recommendation algorithm is validated on two public datasets in this work. By using traditional algorithms and current semantic recommendation algorithms as benchmarks, extensive experiments show that our proposed algorithm consistently outperforms state-of-the-art methods.

Keywords

• Semantic recommendation
• knowledge graphs
• representation learning
• formal concept analysis
• deep matrix decomposition