Survey on Cloud-based Video Suggestions with Categorization of Users in Shared Systems

Abstract

With the rapid growth in multimedia services and the enormous offers of video contents in online social networks, users have difficulty in obtaining their interests. Therefore, various personalized recommendation systems have been proposed. In addition, none of them has considered both the privacy of users' contexts (e,g., social status, ages and hobbies) and video service vendors' repositories, which are extremely sensitive and of significant commercial value. To handle these problems, it's been proposed a cloud-assisted differentially private video recommendation system based on distributed online learning.In our project we proposed the new optimization technique for recommendation. The video recommendation is based on user's behavior (user's interest) and also using the pattern mining for video tag search recommendation. We have search option as sub category search and global search in our application. Facing massive multimedia services and contents in the Internet is based the content provider. In that group of providers we need to find out the irrelevant content promoters. Content promoters are usually trying to promote their contents to social media service or video service sites in internet. In our project Based on the user's interest we can detect and avoid the irrelevant content and content promoters.