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Abstract-Social networking sites helps in developing virtual communities for people to share their thoughts, interest activities or to increase their horizon of camaraderie. Social networking sites come under few of the most frequently browsed categories websites in the world. Nevertheless Social Networking sites are also vulnerable to various problems, threats and attacks such as disclosure of information, identity thefts etc. Privacy practice in social networking sites often come into sight, as information sharing stands in conflict with the disclosure-related misuse. Face book is one such most popular and widely used Social Networking sites which have its own robust set of Privacy mechanisms. Yet they are also prone to various privacy issues and attacks. The impulse in this paper lies in proposing a novel approach for improving the privacy among the social networking sites .The article presents the issues by a novel approach based on tagging and a model based technique based on generalized Gaussian Mixture Model.

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The Web makes superb open doors for organizations to give customized online administrations to their clients.Recommender frameworks intend to consequently create customized recommendations of items administrations to clients (business or person). In spite of the fact that recommender frameworks have been very much contemplated, there are still two difficulties in the improvement of a recommender framework, especially in genuine B2B e-administrations. In Proposed a suggestion system using the quick dissemination and data sharing ability of a huge client organize. The proposed strategy [described as the client driven recommender framework (CRS)] takes after the cooperative separating (CF) rule yet performs appropriated and nearby looks for comparative neighbors over a client organize with a specific end goal to create a suggestion list.