Hierarchical Granulation Recommendation Method Based on Knowledge Graph

Abstract

The recommendation system based on graph neural network is the current research hotspot of data mining applications.The recommendation performance can be improved by combining the graph neural network on the heterogeneous information network(HIN).However,the existing HIN-based recommendation methods often have problems that cannot effectively explain the results of high-level modeling,and manual design of meta-paths requires knowledge of related domains.Therefore,this paper combines the idea of hierarchical granulation andproposes a heterogeneous recommendation method(HKR) based on knowledge graphs.The local context and non-local context are hierarchically granulated,and the coarse-grained representation of user characteristics is learned separately.Then based on the gating mechanism, combining local and non-local attribute node embedding,learning the potential features between users and items,and finally fusing fine-grained features for recommendation.The real experimental results show that the performance of the proposed method is better than the current graph neural network recommendation method based on knowledge graph in many aspects.

Keywords

• hierarchical granulation|knowledge graph|multi-granularity fusion|recommendation system