Web Service Recommendation and QoS Prediction via Matrix Factorization

In web service the recommendation based on Quality of Service are gaining its popularity to favor the customers to identify the high-Quality Services on the internet. We are using the Collaborative Filtering (CF) based techniques to calculate the high level QoS values of web services which cannot be invoked by the user. The basic idea of Collaborative Filtering Techniques is to identify the users with similar QoS experiences and to predict the QoS requirements on web service. To calculate the values of QoS and user requirements will require the parameters of the user which contain some privacy information of the user. Some users are not willing to give such information to the third party. Our main challenge is to provide accurate web service recommendations to the users and preserving the privacy from the third party. To overcome this problem, we identified a new protocol for protecting the privacy and web service recommendation where an untrusted recommendation server is also able to provide the services to the user without knowing the private information of the individual user.