Recently, federated recommendation has become a research hotspot mainly because of users’ awareness of privacy in data. As a recent and important recommendation problem, in heterogeneous one-class collaborative filtering (HOCCF), each user may involve of two different types of implicit feedback, that is, examinations and purchases. So far, privacy-preserving HOCCF has received relatively little attention. Existing federated recommendation works often overlook the fact that some privacy sensitive behaviors such as purchases should be collected to ensure the basic business imperatives in e-commerce for example. Hence, the user privacy constraints can and should be relaxed while deploying a recommendation system in real scenarios. In this article, we study the federated multi-behavior recommendation problem under the assumption that purchase behaviors can be collected. Moreover, there are two additional challenges that need to be addressed when deploying federated recommendation. One is the low storage capacity for users’ devices to store all the item vectors, and the other is the low computational power for users to participate in federated learning. To release the potential of privacy-preserving HOCCF, we propose a novel framework, named discrete federated multi-behavior recommendation (DFMR), which allows the collection of the business necessary behaviors (i.e., purchases) by the server. As to reduce the storage overhead, we use discrete hashing techniques, which can compress the parameters down to 1.56% of the real-valued parameters. To further improve the computation-efficiency, we design a memorization strategy in the cache updating module to accelerate the training process. Extensive experiments on four public datasets show the superiority of our DFMR in terms of both accuracy and efficiency.

近年来，联合推荐成为研究热点，主要是因为用户对数据隐私的认识。作为最近出现的一个重要的推荐问题，在异构一类协同过滤（HOCCF）中，每个用户可能涉及两种不同类型的隐式反馈，即检查和购买。到目前为止，保护隐私的 HOCCF 受到的关注相对较少。现有的联合推荐工作经常忽视这样一个事实：应该收集一些隐私敏感行为（例如购买），以确保电子商务中的基本业务需求。因此，在实际场景中部署推荐系统时，可以而且应该放宽用户隐私约束。在本文中，我们在购买行为可以收集的假设下研究联合多行为推荐问题。此外，在部署联合推荐时还需要解决两个额外的挑战。一是用户设备存储所有项目向量的存储容量低，二是用户参与联邦学习的计算能力低。为了释放隐私保护 HOCCF 的潜力，我们提出了一种新颖的框架，称为离散联合多行为推荐（DFMR），它允许服务器收集业务必要的行为（即购买）。为了减少存储开销，我们使用离散哈希技术，可以将参数压缩到实值参数的1.56%。为了进一步提高计算效率，我们在缓存更新模块中设计了记忆策略来加速训练过程。对四个公共数据集的广泛实验表明了我们的 DFMR 在准确性和效率方面的优越性。