# C3DRec: Cloud-Client Cooperative Deep Learning for Temporal Recommendation in the Post-GDPR Era

arXiv: https://arxiv.org/abs/2101.05641 · HTML: https://mlsanity.com/paper/2101.05641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 18 of 18)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://mlsanity.com/paper/1703.04247/) (2017)
- [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://mlsanity.com/paper/1710.01878/) (2017)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Learning Tree-based Deep Model for Recommender Systems](https://mlsanity.com/paper/1801.02294/) (2018)
- [Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks](https://mlsanity.com/paper/1706.04148/) (2017)
- [DeepCache: Principled Cache for Mobile Deep Vision](https://mlsanity.com/paper/1712.01670/) (2017)
- [A First Look at Deep Learning Apps on Smartphones](https://mlsanity.com/paper/1812.05448/) (2018)
- [TensorFlow.js: Machine Learning for the Web and Beyond](https://mlsanity.com/paper/1901.05350/) (2019)
- [Moving Deep Learning into Web Browser: How Far Can We Go?](https://mlsanity.com/paper/1901.09388/) (2019)
- [Through a Gender Lens: Learning Usage Patterns of Emojis from Large-Scale Android Users](https://mlsanity.com/paper/1705.05546/) (2017)
- [Intermittent Learning: On-Device Machine Learning on Intermittently Powered System](https://mlsanity.com/paper/1904.09644/) (2019)
- [Approximate Query Service on Autonomous IoT Cameras](https://mlsanity.com/paper/1909.00841/) (2019)
- [Mining Behavioral Patterns from Millions of Android Users](https://mlsanity.com/paper/1702.05060/) (2017)
- [A Systematic Analysis of Fine-Grained Human Mobility Prediction with On-Device Contextual Data](https://mlsanity.com/paper/1901.10167/) (2019)

## Abstract (arXiv)

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 C3DRec, a cloud-client cooperative deep learning framework of mining interaction behaviors for recommendation while preserving user privacy. C3DRec 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 C3DRec achieves comparable recommendation accuracy to the centralized approaches, with minimal privacy concern.