TY - JOUR
T1 - Knowledge-Aware Multi-view Contrastive Learning for Recommendation
AU - Xie, Xiang
AU - Xie, Zhenping
AU - Liu, Yuan
AU - Wang, Jia
AU - Zhan, Qianyi
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/4
Y1 - 2025/4
N2 - Knowledge-aware Recommendation (KGR) aims to utilize a knowledge graph to provide rich side information for items in a recommendation system and construct a unified graph containing users, items, and entities. In this paper, we present a new graph neural network for user-item-entity interaction modeling, named Graph Attention Intent Network, it employs different strategies to aggregate user and item information to generate high-quality representations. Typically, the description of user-item interactions is modeled as a bipartite graph, which overlooks the relations between users and between items, a significant aspect of realistic recommendation. Therefore, we propose a framework, named knowledge-aware multi-view contrastive learning for recommendation. It can explore effective user-user and item-item relations in the heterogeneous network of KGR, construct a user social graph and an item similarity graph, and combine the information of the two views into user-item-entity interaction modeling to enhance the representation of users and items. We introduce cross-graph contrastive learning to facilitate the integration of heterogeneous information while alleviating the sparse labeling problem of recommendation tasks. Experimental results on three benchmark datasets show that our model is more effective than other state-of-the-art models.
AB - Knowledge-aware Recommendation (KGR) aims to utilize a knowledge graph to provide rich side information for items in a recommendation system and construct a unified graph containing users, items, and entities. In this paper, we present a new graph neural network for user-item-entity interaction modeling, named Graph Attention Intent Network, it employs different strategies to aggregate user and item information to generate high-quality representations. Typically, the description of user-item interactions is modeled as a bipartite graph, which overlooks the relations between users and between items, a significant aspect of realistic recommendation. Therefore, we propose a framework, named knowledge-aware multi-view contrastive learning for recommendation. It can explore effective user-user and item-item relations in the heterogeneous network of KGR, construct a user social graph and an item similarity graph, and combine the information of the two views into user-item-entity interaction modeling to enhance the representation of users and items. We introduce cross-graph contrastive learning to facilitate the integration of heterogeneous information while alleviating the sparse labeling problem of recommendation tasks. Experimental results on three benchmark datasets show that our model is more effective than other state-of-the-art models.
KW - Constrastive learning
KW - Graph neural network
KW - Knowledge graph
KW - Multi-view representation learning
KW - Recommendation
UR - http://www.scopus.com/inward/record.url?scp=105001648480&partnerID=8YFLogxK
U2 - 10.1007/s11063-025-11750-0
DO - 10.1007/s11063-025-11750-0
M3 - Article
AN - SCOPUS:105001648480
SN - 1370-4621
VL - 57
JO - Neural Processing Letters
JF - Neural Processing Letters
IS - 2
M1 - 36
ER -