$C^3DRec$：后GDPR时代用于时间推荐的云-客户端协同深度学习
$C^3DRec$: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era
摘要：
移动设备使用户能够随时随地检索信息。考虑到移动用户的临时需求和碎片化使用模式，提出了基于用户短期交互行为的时态兴趣学习方法，以提高移动设备上信息检索的效率.然而，隐私保护法律法规的实施，如GDPR，可能会使临时推荐的成功实践黯然失色。原因在于，现有的推荐系统需要在集中式服务器中收集和处理用户数据，但是用于临时推荐的交互行为数据通常是非事务性数据，根据GDPR，在没有用户的明确许可的情况下不允许收集这些数据。因此，如果用户不允许服务收集他们的交互行为数据，时间推荐就无法工作。为实现后GDPR时代的时态推荐，提出了一种基于云-客户端协同的深度学习框架$C^3DRec$，用于在保护用户隐私的同时挖掘交互行为进行推荐。$C^3DRec$使用GDPR之前收集的数据在集中式服务器上构建全局推荐模型，并使用GDPR之后收集的数据直接在各个本地设备上微调模型。我们设计了两种模式来完成推荐，即拉模式，其中候选项目被拉到设备上，并送入本地模型，以获得推荐项目，推模式，其中本地模型的输出推到服务器上，并与候选项目相结合，以获得推荐项目。评估结果表明，$C^3DRec$实现了类似的推荐精度集中的方法，以最小的隐私问题。
英文摘要：
Mobile devices enable users to retrieve information at any time and any place. Considering the occasional requirements and fragmentation usage pattern of mobile users, temporal recommendation techniques are proposed to improve the efficiency of information retrieval on mobile devices by means of accurately recommending items via learning temporal interests with short-term user interaction behaviors. However, the enforcement of privacy-preserving laws and regulations, such as GDPR, may overshadow the successful practice of temporal recommendation. The reason is that state-of-the-art recommendation systems require to gather and process the user data in centralized servers but the interaction behaviors data used for temporal recommendation are usually non-transactional data that are not allowed to gather without the explicit permission of users according to GDPR. As a result, if users do not permit services to gather their interaction behaviors data, the temporal recommendation fails to work. To realize the temporal recommendation in the post-GDPR era, this paper proposes $C^3DRec$, a cloud-client cooperative deep learning framework of mining interaction behaviors for recommendation while preserving user privacy. $C^3DRec$ constructs a global recommendation model on centralized servers using data collected before GDPR and fine-tunes the model directly on individual local devices using data collected after GDPR. We design two modes to accomplish the recommendation, i.e. pull mode where candidate items are pulled down onto the devices and fed into the local model to get recommended items, and push mode where the output of the local model is pushed onto the server and combined with candidate items to get recommended ones. Evaluation results show that $C^3DRec$ achieves comparable recommendation accuracy to the centralized approaches, with minimal privacy concern.
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