Federated recommender systems have distinct advantages in terms of privacy protection over traditional recommender systems that are centralized at a data center. However, previous work on federated recommender systems does not fully consider the limitations of storage, RAM, energy and communication bandwidth in a mobile environment. The scales of the models proposed are too large to be easily run on mobile devices. And existing federated recommender systems need to fine-tune recommendation models on each device, making it hard to effectively exploit collaborative filtering information among users/devices. Our goal in this paper is to design a novel federated learning framework for rating prediction (RP) for mobile environments. We introduce a federated matrix factorization (MF) framework, named meta matrix factorization (MetaMF). Given a user, we first obtain a collaborative vector by collecting useful information with a collaborative memory module. Then, we employ a meta recommender module to generate private item embeddings and a RP model based on the collaborative vector in the server. To address the challenge of generating a large number of high-dimensional item embeddings, we devise a rise-dimensional generation strategy that first generates a low-dimensional item embedding matrix and a rise-dimensional matrix, and then multiply them to obtain high-dimensional embeddings. We use the generated model to produce private RPs for the given user on her device. MetaMF shows a high capacity even with a small RP model, which can adapt to the limitations of a mobile environment. We conduct extensive experiments on four benchmark datasets to compare MetaMF with existing MF methods and find that MetaMF can achieve competitive performance. Moreover, we find MetaMF achieves higher RP performance over existing federated methods by better exploiting collaborative filtering among users/devices.

翻译：联邦推荐系统在隐私保护方面比传统集中式数据中心推荐系统具有明显优势。然而，现有联邦推荐系统研究并未充分考虑移动环境中存储、内存、能量和通信带宽的限制。所提模型规模过大，难以在移动设备上轻松运行。且现有联邦推荐系统需在每个设备上对推荐模型进行微调，难以有效利用用户/设备间的协同过滤信息。本文旨在为移动环境下的评分预测任务设计一种新型联邦学习框架。我们提出了一种名为元矩阵分解的联邦矩阵分解框架。对于给定用户，首先通过协同记忆模块收集有用信息获得协同向量。随后，在服务器端基于该协同向量利用元推荐模块生成私有物品嵌入与评分预测模型。为解决生成大量高维物品嵌入的挑战，设计了升维生成策略：首先生成低维物品嵌入矩阵与升维矩阵，再将两者相乘得到高维嵌入。利用生成模型在用户设备上产生私有评分预测结果。即使采用小规模评分预测模型，元矩阵分解仍展现出高容量，可适应移动环境限制。在四个基准数据集上的大量实验表明，与现有矩阵分解方法相比，元矩阵分解取得了竞争性能。此外，通过更高效地利用用户/设备间协同过滤信息，元矩阵分解较现有联邦方法实现了更高的评分预测性能。