  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.
