Social recommendations have been widely adopted in substantial domains.
Recently, graph neural networks (GNN) have been employed in recommender systems
due to their success in graph representation learning. However, dealing with
the dynamic property of social network data is a challenge. This research
presents a novel method that provides social recommendations by incorporating
the dynamic property of social network data in a heterogeneous graph. The model
aims to capture user preference over time without going through the
complexities of a dynamic graph by adding period nodes to define users'
long-term and short-term preferences and aggregating assigned edge weights. The
model is applied to real-world data to argue its superior performance.
Promising results demonstrate the effectiveness of this model.

通过在异构图中结合社交网络数据的动态属性，本研究提出了一种新的方法来提供社交推荐，旨在捕捉用户的长期和短期偏好并聚合分配的边权重，以实现对时间变化的用户偏好的建模，实验结果证明了该模型的有效性。

通过动态时期定义的长期和短期偏好的异构图建模的社交推荐

Social Recommendation through Heterogeneous Graph Modeling of the  Long-term and Short-term Preference Defined by Dynamic Periods

Social recommendations utilize social relations to enhance the representation
learning for recommendations. Most social recommendation models unify user
representations for the user-item interactions (collaborative domain) and
social relations (social domain). However, such an approach may fail to model
the users heterogeneous behavior patterns in two domains, impairing the
expressiveness of user representations. In this work, to address such
limitation, we propose a novel Disentangled contrastive learning framework for
social Recommendations DcRec. More specifically, we propose to learn
disentangled users representations from the item and social domains. Moreover,
disentangled contrastive learning is designed to perform knowledge transfer
between disentangled users representations for social recommendations.
Comprehensive experiments on various real-world datasets demonstrate the
superiority of our proposed model.

本研究提出了一种新颖的解缠式对比学习框架，名为 DcRec，该框架可以从物品和社交领域中学习解缠式用户表示，并在社交推荐中进行知识转移。实验证明，该模型在各种真实世界数据集上具有优越性。

社交推荐的解耦对比学习

Disentangled Contrastive Learning for Social Recommendation

With the recent surge of social networks like Facebook, new forms of
recommendations have become possible - personalized recommendations of ads,
content, and even new friend and product connections based on one's social
interactions. Since recommendations may use sensitive social information, it is
speculated that these recommendations are associated with privacy risks. The
main contribution of this work is in formalizing these expected trade-offs
between the accuracy and privacy of personalized social recommendations.
In this paper, we study whether "social recommendations", or recommendations
that are solely based on a user's social network, can be made without
disclosing sensitive links in the social graph. More precisely, we quantify the
loss in utility when existing recommendation algorithms are modified to satisfy
a strong notion of privacy, called differential privacy. We prove lower bounds
on the minimum loss in utility for any recommendation algorithm that is
differentially private. We adapt two privacy preserving algorithms from the
differential privacy literature to the problem of social recommendations, and
analyze their performance in comparison to the lower bounds, both analytically
and experimentally. We show that good private social recommendations are
feasible only for a small subset of the users in the social network or for a
lenient setting of privacy parameters.

本文研究了基于社交网络的个性化推荐 —— 社交推荐算法在保护隐私的同时对推荐准确度的影响，并量化了算法的效用损失。研究表明，只有在隐私参数比较宽松的情况下或为社交网络中的一小部分用户提供可行的好的私密社交推荐。