Privacy-preserving techniques in recommender systems: state-of-the-art review and future research agenda
D Pramod - Data Technologies and Applications, 2022 - emerald.com
Purpose This study explores privacy challenges in recommender systems (RSs) and how
they have leveraged privacy-preserving technology for risk mitigation. The study also …
they have leveraged privacy-preserving technology for risk mitigation. The study also …
A comprehensive survey on privacy-preserving techniques in federated recommendation systems
Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …
address various challenges. One such development is the use of federated learning for …
Differentially private locality sensitive hashing based federated recommender system
Recommender systems are important applications in big data analytics because accurate
recommendation items or high‐valued suggestions can bring high profit to both commercial …
recommendation items or high‐valued suggestions can bring high profit to both commercial …
FedRec+: Enhancing Privacy and Addressing Heterogeneity in Federated Recommendation Systems
Preserving privacy and reducing communication costs for edge users pose significant
challenges in recommendation systems. Although federated learning has proven effective in …
challenges in recommendation systems. Although federated learning has proven effective in …
Sequential POI Recommend Based on Personalized Federated Learning
Q Dong, B Liu, X Zhang, J Qin, B Wang - Neural Processing Letters, 2023 - Springer
Point-of-Interest recommendation system (POI-RS) aims at mining users' potential preferred
venues. Many works introduce Federated Learning (FL) into POI-RS for privacy-protecting …
venues. Many works introduce Federated Learning (FL) into POI-RS for privacy-protecting …
A Locality-Sensitive Hashing based Collaborative Recommendation Method for Responsible AI Driven Recommender Systems
W Lin, X Zhou, L Sun, L Qi, SB Tsai… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
As one of the most representative recommendation solutions, traditional collaborative
filtering models typically have limitations in dealing with large-scale, sparse data to capture …
filtering models typically have limitations in dealing with large-scale, sparse data to capture …
Understanding and Scaling Collaborative Filtering Optimization from the Perspective of Matrix Rank
Collaborative Filtering (CF) methods dominate real-world recommender systems given their
ability to learn high-quality, sparse ID-embedding tables that effectively capture user …
ability to learn high-quality, sparse ID-embedding tables that effectively capture user …
FedHAP: Federated Hashing With Global Prototypes for Cross-Silo Retrieval
Deep hashing has been widely applied in large-scale data retrieval due to its superior
retrieval efficiency and low storage cost. However, data are often scattered in data silos with …
retrieval efficiency and low storage cost. However, data are often scattered in data silos with …
Clustered Federated Learning with Inference Hash Codes Based Local Sensitive Hashing
Federated Learning (FL) is a distributed paradigm enabling clients to train a global model
collaboratively while protecting client privacy. During the FL training process, the statistical …
collaboratively while protecting client privacy. During the FL training process, the statistical …