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.

翻译：社交推荐系统因融合用户间的社交信息以提升推荐效果，已在众多在线网络服务中引起广泛关注。尽管现有解决方案取得了显著进展，但我们认为当前方法存在两个局限：（1）现有的社交感知推荐模型仅考虑物品间的协同相似性，而如何在当前推荐范式中纳入物品间的语义相关性仍鲜有探索。（2）当前的社交推荐系统忽视了异构关系（如社交连接、用户-物品交互）中潜在因素的耦合问题。学习具有关系异构性的解耦表征对社交推荐提出了重大挑战。本研究设计了一种融合潜在记忆单元的解耦图神经网络（DGNN），该模型能够为异构类型的用户和物品连接维护分解后的表征。此外，我们在图神经架构下设计了新的记忆增强消息传播与聚合方案，从而能够以全自动方式将语义相关性递归地提炼到用户和物品的表征中。在三个基准数据集上的广泛实验验证了该模型的有效性，其性能显著超越了最先进的推荐技术。源代码已公开发布于：https://github.com/HKUDS/DGNN。