Survey on Cloud-Based Video Suggestions with Categorization of Users in Shared Systems
Muthu Pradeepa S P , Priya R , Sangeetha S , Prabhu S
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.
Video recommendation, Cloud sharing, User based recommendation, Categorized recommendation.
 Ming Cheung,James She, and Zhanming Jie, “Connection discovery using big data of user-shared images in social media,” Multimedia, IEEE Transactions on.vol. 17, no. 9, pp. 1417-1428, 2015.  Arjmand Samuel, Muhammad I. Sarfraz, Saleh Basalamah, Arif Ghafoor,“A framework for composition and enforcement of privacy-aware and context-driven authorization mechanism for multimedia big data,” Multimedia, IEEE Transactions on. vol.17, no. 9, pp. 1484- 1494, 2015.  Marko Tkalcic, Andrej Košir, Jurij Tasic, “Affective labeling in a content-based recommender system for images,” Multimedia, IEEE Transactions on. vol. 15, no. 2, pp. 391-400, 2013.  Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei, “Author topic model-based collaborative filtering for personalized POI recommendations,” Multimedia, IEEE Transactions on. vol. 17, no. 6, pp. 907-918, 2015.  Zekeriya Erkin, Michael Beye, Thijs Veugen, “Generating private recommendations efficiently using homomorphic encryption and data packing,” in IEEE Transactions onInformation Forensics and Security, vol. 7, no. 3, pp. 1053- 1066, 2012.  Frank McSherry, Kunal Talwar, “Mechanism design via differential privacy,”in Foundations Computer Science, FOCS07. 48th Annual IEEE Symposium on. IEEE, pp. 94- 103, 2007.  Geonhyeok Go, Joonhyuk Yang, Hyunwoo Park, Sangki Han, “Using onlinemedia sharing behavior as implicit feedback for collaborative filtering,” in Social Computing (SocialCom), 2010 IEEE Second International Conference on. IEEE, pp. 439-445, 2010.  Ziqi Wang, Yuwei Tan, Ming Zhang, “Graph-based recommendation on social networks,” Web Conference (APWEB), 2010 12th International Asia Pacific. IEEE, pp. 116-122. 2010.  Cem Tekin, Mihaela van der Schaar, “Distributed online big data classification using context information,” in Communication, Control, and Computing (Allerton), 51st Annual Allerton Conference on IEEE, pp. 1435-1442, 2013.  Cem Tekin, Simpson Zhang, and Mihaela van der Schaar, “Distributed online learning is social recommender systems,” in IEEE Journal of Selected Topics in Signal Processing, vol. 8, no. 4, pp. 638-652, 2014
[Muthu Pradeepa S P , Priya R , Sangeetha S , Prabhu S (2017) Survey on Cloud-Based Video Suggestions with Categorization of Users in Shared Systems IJIREM Vol-4 Issue-1 Page No-589-592] (ISSN 2350 - 0557). www.ijirem.org
Muthu Pradeepa S P
UG Student, Department of Computer Science and Engineering, S.A.Engineering College, Poonamallee- Avadi Road, Veeraraghavapuram, Thiruverkadu Post, Chennai – 600077