Proceedings ArticleDOI
DeepRec: On-device Deep Learning for Privacy-Preserving Sequential Recommendation in Mobile Commerce
Jialiang Han,Yun Ma,Qiaozhu Mei,Xuanzhe Liu +3 more
- pp 900-911
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TLDR
This paper proposes DeepRec, an on-device deep learning framework of mining interaction behaviors for sequential recommendation without sending any raw data or intermediate results out of the device, preserving user privacy maximally.Abstract:
Sequential recommendation techniques are considered to be a promising way of providing better user experience in mobile commerce by learning sequential interests within user historical interaction behaviors. However, the recently increasing focus on privacy concerns, such as the General Data Protection Regulation (GDPR), can significantly affect the deployment of state-of-the-art sequential recommendation techniques, because user behavior data are no longer allowed to be arbitrarily used without the user’s explicit permission. To address the issue, this paper proposes DeepRec, an on-device deep learning framework of mining interaction behaviors for sequential recommendation without sending any raw data or intermediate results out of the device, preserving user privacy maximally. DeepRec constructs a global model using data collected before GDPR and fine-tunes a personal model continuously on individual mobile devices using data collected after GDPR. DeepRec employs the model pruning and embedding sparsity techniques to reduce the computation and network overhead, making the model training process practical on computation-constraint mobile devices. Evaluation results show that DeepRec can achieve comparable recommendation accuracy to existing centralized recommendation approaches with small computation overhead and up to 10x reduction in network overhead.read more
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Proceedings ArticleDOI
On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation
TL;DR: A self-supervised knowledge distillation framework is developed which enables the compressed model (student) to distill the essential information lying in the raw data, and improves the long-tail item recommendation through an embedding-recombination strategy with the original model (teacher).
Journal ArticleDOI
Recommendation Systems: An Insight Into Current Development and Future Research Challenges
TL;DR: In this paper , an extension to the standard taxonomy is presented to better reflect the latest research trends, including the diverse use of content and temporal information, and the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks.
Journal ArticleDOI
Recommendation Systems: An Insight Into Current Development and Future Research Challenges
TL;DR: A gentle introduction to recommendation systems is provided, describing the task they are designed to solve and the challenges faced in research, and an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information.
Journal ArticleDOI
A Generic Federated Recommendation Framework via Fake Marks and Secret Sharing
TL;DR: This paper proposes a lossless and generic federated recommendation framework via fake marks and secret sharing (FMSS), which can not only protect the two types of users’ privacy, without sacrificing the recommendation performance, but can also be applied to most recommendation algorithms for rating prediction, item ranking, and sequential recommendation.
Journal ArticleDOI
Efficient On-Device Session-Based Recommendation
TL;DR: Extensive experimental results on two benchmark datasets demonstrate that compared with existing methods, the proposed on-device recommender not only achieves an 8x inference speedup with a large compression ratio but also shows superior recommendation performance.
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