Recommender systems have shown a lot of awareness in the past decade. Due to their great business value, recommender systems have also been successfully deployed in business, such as product recommendation at flipkart, HomeShop18, and music recommendation at Last.fm, Pandora, and movie recommendation at Flixstreet, MovieLens, and Jinni. In the past few years, the incredible growth of Web 2.0 web sites and applications constitute new challenges for Traditional recommender systems. Traditional recommender systems always ignore social interaction among users. But in our real life, when we are asking our friends or looking opinions, reviews for recommendations of Mobile or heart touching music, movies, electronic gadgets, restaurant, book, games, software Apps, we are actually using social information for recommendations. In this paper social popularity factor are incorporated in SVD++ factorization method as implicit feedback to improve accuracy and scalability of recommendations.

Contexts and social web information have been recognized to be valuable information for making perfect recommender system. Context-aware recommender systems (CARS) have been implemented in different applications and domains which improve the performance of recommendations. Context-aware approaches have been successfully applied in various domains such as music, movies, mobile recommendations, personalized shopping assistants, conversational and interactional services, social rating services and multimedia. The recommender systems are widely being used for products, content and services recommendations. Successful deployment of recommender system in social web and many commercial website like Amazon.com, flipkart, HomeShop18 and numerous different sectors have already done. The growth of the social web has revolutionized the architecture of sharing and association in the web, making it essential to reiterate recommendation. If recommender systems have established their key role in providing the user access to resources on the web, when sharing resources has turn into social, it is likely for recommendation techniques in the social web should consider social popularity factor and the relationships among users to compute their predictions. In this paper contextual information are being included in social popularity based SVD++ model to improve accuracy and scalability of recommendations.