Privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems

Abstract With the rapid growth of public cloud offerings, how to design effective prediction models that provide appropriate recommendations for potential users has become more and more important. In dynamic cloud environment, both of user behaviors and service performance are sensitive to contextual information, such as geographic location information. In addition, the increasing number of attacks and security threats also brought the problem that how to protect critical information assets such as sensitive data, cloud resources and communication in a more effective and secure manner. In view of these challenges, we propose a privacy-preserving and sparsity-aware location-based prediction method for collaborative recommender systems. Specifically, our method is designed as a three-phase process: Firstly, two privacy-preserving mechanisms, i.e., a randomized data obfuscation technique and a region aggregation strategy are presented to protect the private information of users and deal with the data sparsity problem. Then a location-aware latent factor model based on tensor factorization is applied to explore the spatial similarity relationships between services. Finally, predictions are made based on both global and spatial nearest neighbors. Experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method achieves decent prediction accuracy on the premise of privacy preservation.