Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user’s privacy. However, how to simultaneously protect user’s privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user’s privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the
$L_1$
-norm low-rank matrix factorization to improve the model’s robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.