计算机科学
推荐系统
图形
水准点(测量)
情报检索
相似性(几何)
语义学(计算机科学)
关系(数据库)
社交网站
人工智能
万维网
社会化媒体
理论计算机科学
数据挖掘
图像(数学)
程序设计语言
地理
大地测量学
作者
Lianghao Xia,Yizhen Shao,Chao Huang,Yong Xu,Huance Xu,Jian Pei
标识
DOI:10.1109/icde55515.2023.00180
摘要
Social recommender systems have drawn a lot of attention in many online web services, because of the incorporation of social information between users in improving recommendation results. Despite the significant progress made by existing solutions, we argue that current methods fall short in two limitations: (1) Existing social-aware recommendation models only consider collaborative similarity between items, how to incorporate item-wise semantic relatedness is less explored in current recommendation paradigms. (2) Current social recommender systems neglect the entanglement of the latent factors over heterogeneous relations (e.g., social connections, user-item interactions). Learning the disentangled representations with relation heterogeneity poses great challenge for social recommendation. In this work, we design a Disentangled Graph Neural Network (DGNN) with the integration of latent memory units, which empowers DGNN to maintain factorized representations for heterogeneous types of user and item connections. Additionally, we devise new memory-augmented message propagation and aggregation schemes under the graph neural architecture, allowing us to recursively distill semantic relatedness into the representations of users and items in a fully automatic manner. Extensive experiments on three benchmark datasets verify the effectiveness of our model by achieving great improvement over state-of-the-art recommendation techniques. The source code is publicly available at: https://github.com/HKUDS/DGNN.
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