基于知识图谱的兴趣捕捉推荐算法

Interest Capturing Recommendation Based on Knowledge Graph

知识图谱作为一种辅助信息,可以为推荐系统提供更多的上下文信息和语义关联信息,从而提高推荐的准确性和可解释性。通过将项目映射到知识图谱中,推荐系统可以将从知识图谱中学习到的外部知识注入到用户和项目的表示中,进而增强用户和项目的表示。但在学习用户偏好时,基于图神经网络的知识图谱推荐主要通过项目实体利用知识图谱中的属性信息和关系信息等知识信息。由于用户节点并不与知识图谱直接相连,这就导致不同的关系信息和属性信息在语义上和用户偏好方面是独立的,缺乏关联。这表明,基于知识图谱的推荐难以根据知识图谱中的信息来准确捕获用户的细粒度偏好。因此,针对用户细粒度兴趣难以捕捉的问题,提出了一种基于知识图谱的兴趣捕捉推荐算法。该算法利用知识图谱中的关系和属性信息来学习用户的兴趣,并增强用户和项目的嵌入表示。为了充分利用知识图谱中的关系信息,设计了关系兴趣模块以学习用户对不同关系的细粒度兴趣。该模块将每个兴趣表示为知识图谱中关系向量的组合,并利用图卷积神经网络在用户项目图和知识图谱中传递用户兴趣以学习用户和项目的嵌入表示。此外,还设计了属性兴趣模块以学习用户对不同属性的细粒度兴趣。该模块采用切分嵌入的方法为用户和项目匹配与之相似的属性,并使用与关系兴趣模块中相似的方法进行消息传播。最终,在两个基准数据集上进行实验,实验结果验证了该方法的有效性和可行性。

As a kind of auxiliary information,knowledge graph can provide more context information and semantic association information for the recommendation system,thereby improving the accuracy and interpretability of the recommendation.By mapping 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 relationship 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 relational 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 conducted on two benchmark datasets,and the experimental results demonstrate the effectiveness and feasibility of the proposed method.