Interest Capturing Recommendation Based on Knowledge Graph
As a kind of auxiliary information,knowledge graph can provide more context information and semantic association in-formation for the recommendation system,thereby improving the accuracy and interpretability of the recommendation.By map-ping items into knowledge graphs,recommender systems can inject external knowledge learned from knowledge graphs into user and item representations,thereby enhancing user and item representations.However,when learning user preferences,the know-ledge graph recommendation based on graph neural network mainly utilizes knowledge information such as attribute and relation-ship information in the knowledge graph through project entities.Since user nodes are not directly connected to the knowledge graph,different relational and attribute information are semantically independent and lack correlation regarding user preferences.It is difficult for the recommendation based on the knowledge graph to accurately capture user's fine-grained preferences based on the information in the knowledge graph.Therefore,to address the difficulty in capturing users'fine-grained interests,this paper proposes an interest-capturing recommendation algorithm based on a knowledge graph(KGICR).The algorithm leverages the re-lational and attribute information in knowledge graphs to learn user interests and improve the embedding representations of users and items.To fully utilize the relational information in the knowledge graph,a relational interest module is designed to learn users'fine-grained interests in different relations.This module represents each interest as a combination of relation vectors in the knowledge graph and employs a graph convolutional neural network to transfer user interests in the user-item graph and the knowledge graph to learn user and item embedding representations.Furthermore,an attribute interest module is also designed to learn users'fine-grained interests in different attributes.This module matches users and items with similar attributes by splitting and embedding and uses a similar method to the relational interest module for message propagation.Finally,experiments are con-ducted on two benchmark datasets,and the experimental results demonstrate the effectiveness and feasibility of the proposed method.
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