西安电子科技大学学报（自然科学版）2025，Vol.52Issue(1)：163-180,18.DOI:10.19665/j.issn1001-2400.20241101
端到端异构图信息协同过滤推荐
End-to-end heterogeneous graph information collaborative filtering for recommendation
摘要
Abstract
Knowledge Graphs(KG)have emerged as a pivotal trend in uncovering intricate relations between items within recommendation scenarios.While models such as the Knowledge Graph Attention Network(KGAT)focus on establishing first-order relations,their limitations become apparent when attempting to capture collaborative information inherent in high-order relations.Existing KG-based models,including the KGAT,often treat interactive behavior as a mere KG relation.They seamlessly fuse user-item bipartite graphs with knowledge graphs into a unified fusion graph,but neglect the inherent heterogeneity between graph structures.This oversight results in a weakened ability to preserve graph-specific properties.In response to these challenges,we delve into a comprehensive analysis of the latent disparities and connections between user-item interactions and relation links.We introduce a groundbreaking message propagation mechanism known as Heterogeneous Graph Information Collaborative Filtering(HGICF).This mechanism seamlessly propagates collaborative features derived from user-item behaviors and KG side information within a unified model.Unlike existing approaches,HGICF not only upholds the intrinsic attributes of inner-graph structures but also facilitates the aggregation of cross-graph information.To gain a deeper understanding of the collaborative dynamics between knowledge graphs and bipartite graphs,we introduce shared feature collaborative filtering layers.These layers are designed to adapt to various data structures and requirements by allowing different layers to be set based on the specificities of the underlying information,which leads to a flexible and adaptive model capable of capturing the nuances of diverse data sources.Extensive experiments conducted validate the superior performance of HGICF over existing state-of-the-art methods.The proposed model excels in preserving the intricate relationships within knowledge graphs and bipartite graphs,showcasing its efficacy in collaborative filtering scenarios.By addressing the limitations of current KG-based models,HGICF stands as a significant advancement in the realm of recommendation systems,offering a more robust and nuanced approach to collaborative information modeling.关键词
信息系统/推荐系统/图神经网络/知识图谱/协作过滤Key words
information systems/recommendation system/graph neural network/knowledge graph/collaborative filtering分类
信息技术与安全科学引用本文复制引用
陈宸,成蓉,宋彬..端到端异构图信息协同过滤推荐[J].西安电子科技大学学报（自然科学版）,2025,52(1):163-180,18.基金项目
国家自然科学基金(62372357) （62372357）
