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Nowadays, in online social networks, there is an instantaneous extension of multimedia services, and there are huge offers of video contents, which have hindered users to acquire their interests. To solve these problems, different personalized recommendation systems have been suggested. All the personalized recommendation systems which have been suggested are not efficient and they have significantly retarded the video recommendation process. So, to solve this difficulty, context extractor-based video recommendation system on cloud has been proposed in this paper. Further, the system has server selection technique to handle the overload program and make it balanced. This paper explains the mechanism used to minimize network overhead, and recommendation process is done by considering the context details of the users; it also uses rule-based process and different algorithms used to achieve the objective. The videos will be stored in the cloud and through application videos will be dumped into cloud storage by reading, coping, and storing processes.

Design of Context Cluster and Load Balanced Video Recommendation System in Cloud Computing