A Review on Personalized Approach for Solving Recommendation System Problems Combining User Interest and Social Circle

Rapid growth of information generated by online social networks leads to increase in demand of effective recommender systems to give accurate results. Traditional techniques become unqualified because they do not consider data of social relation in the social network for giving recommendation; existing social recommendation techniques consider social network structure, but social perspective has not been fully measured by these techniques. It its noteworthy and challenging to fuse social contextual factors which are derived from users motivation of social activities into social recommendation. With the introduction and popularity of social network, ever more users like to share their real life experiences, such as blogs, ratings and reviews. New latest aspects of social networking like interpersonal influence and interest based on circles of friends carry opportunities and challenges for recommender system (RS) to resolve the cold start and sparsity problem of datasets. Several of the social factors have been used in Recommendation Systems; but still they have not been completely measured. This paper gives review on, various recommendation techniques and main three social aspects, User personal interest, interpersonal interest similarity, as well as interpersonal influence, and how these factors are fuse into a combined personalized recommendation model to give the recommendations to the user.