论文标题
通过联合表示学习，用户聚类和模型适应的个性化联合建议
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation
论文作者
论文摘要
联合建议在推荐系统中应用联合学习技术，以通过在用户设备和中央服务器之间交换模型而不是原始用户数据来帮助保护用户隐私。由于用户属性和本地数据的异质性，获得个性化模型对于帮助提高联合建议性能至关重要。在本文中，我们通过联合表示学习，用户聚类和模型适应性提出了一个基于图形神经网络的个性化联合建议（PERPEDREC）框架。具体而言，我们构建了一个协作图，并结合了属性信息，以通过联合GNN共同学习表示形式。基于这些学习的表示形式，我们将用户聚集到不同的用户组中，并为每个集群学习个性化模型。然后，每个用户通过组合全局联合模型，集群级联合模型和用户的微调本地模型来学习个性化模型。为了减轻繁重的沟通负担，我们从每个集群中巧妙地选择了一些代表用户（而不是随机选择的用户）参加培训。现实世界数据集的实验表明，我们提出的方法比现有方法实现了卓越的性能。
Federated recommendation applies federated learning techniques in recommendation systems to help protect user privacy by exchanging models instead of raw user data between user devices and the central server. Due to the heterogeneity in user's attributes and local data, attaining personalized models is critical to help improve the federated recommendation performance. In this paper, we propose a Graph Neural Network based Personalized Federated Recommendation (PerFedRec) framework via joint representation learning, user clustering, and model adaptation. Specifically, we construct a collaborative graph and incorporate attribute information to jointly learn the representation through a federated GNN. Based on these learned representations, we cluster users into different user groups and learn personalized models for each cluster. Then each user learns a personalized model by combining the global federated model, the cluster-level federated model, and the user's fine-tuned local model. To alleviate the heavy communication burden, we intelligently select a few representative users (instead of randomly picked users) from each cluster to participate in training. Experiments on real-world datasets show that our proposed method achieves superior performance over existing methods.
