Decentralized Gradient-Quantization Based Matrix Factorization for Fast Privacy-Preserving Point-of-Interest Recommendation.
Xuebin ZhouZhibin HuJin HuangJian ChenPublished in: J. Artif. Intell. Res. (2023)
Keyphrases
- privacy preserving
- matrix factorization
- collaborative filtering
- recommender systems
- item recommendation
- data sparsity
- privacy preserving data mining
- latent factor models
- vertically partitioned data
- privacy preservation
- low rank
- multi party
- sensitive information
- negative matrix factorization
- privacy concerns
- recommendation systems
- private information
- user preferences
- factorization methods
- data privacy
- implicit feedback
- private data
- nonnegative matrix factorization
- privacy protection
- sensitive data
- privacy issues
- privacy sensitive
- privacy preserving association rule mining
- missing data
- tensor factorization
- latent factors
- search engine
- horizontally partitioned data
- scalar product
- secure multiparty computation