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.

中文翻译：

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$ C ^ 3DRec $：GDPR后时代的云客户端合作深度学习以进行时间推荐

移动设备使用户可以随时随地检索信息。考虑到移动用户的偶尔需求和碎片使用模式，提出了时间推荐技术，以通过学习具有短期用户交互行为的时间兴趣来精确推荐项目，从而提高移动设备上信息检索的效率。但是，保护隐私的法律和法规（例如GDPR）的执行可能会掩盖临时推荐的成功实践。原因是，最新的推荐系统需要在集中式服务器中收集和处理用户数据，但是用于时间推荐的交互行为数据通常是非事务性数据，未经以下方面的明确许可，不允许收集这些数据：用户根据GDPR。结果，如果用户不允许服务收集其交互行为数据，则临时推荐将无法正常工作。为了实现后GDPR时代的时间推荐，本文提出了$ C ^ 3DRec $，这是一种云-客户合作深度学习框架，用于挖掘交互行为的推荐行为，同时保留用户隐私。$ C ^ 3DRec $使用GDPR之前收集的数据在集中式服务器上构建全局推荐模型，并使用GDPR之后收集的数据直接在单个本地设备上微调该模型。我们设计了两种模式来完成推荐，即拉模式（将候选项目下拉到设备上并馈入本地模型以获取推荐项目）和推模式（将本地模型的输出推入服务器并与之组合）候选项目以获得推荐项目。评估结果表明，$ C ^ 3DRec $可以达到与集中式方法相当的推荐准确性，并且对隐私的关注最小。推送模式，将本地模型的输出推送到服务器上，并与候选项目组合以获得推荐的项目。评估结果表明，$ C ^ 3DRec $可以达到与集中式方法相当的推荐准确性，并且对隐私的关注最小。推送模式，将本地模型的输出推送到服务器上，并与候选项目组合以获得推荐的项目。评估结果表明，$ C ^ 3DRec $可以达到与集中式方法相当的推荐准确性，并且对隐私的关注最小。