IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Cloud based mobile multimedia recommendation system with user behavior information
- 1. GLOBALSOFT TECHNOLOGIES Cloud-Based Mobile Multimedia Recommendation System with User Behavior Information Abstract: Facing massive multimedia services and contents in the Internet, users usually waste a lot of time to obtain their interests. Therefore, various context-aware recommendation systems have been proposed. Most of those proposed systems deploy a large number of context collectors at terminals and access networks. However, the context collecting and exchanging result in heavy network overhead, and the context processing consumes huge computation. Content pollution may jeopardize the trust of users on the system we provide a characterization of content, individual, and social attributes that help distinguish each user class. Classification approach succeeds at separating spammers and promoters video search systems can be fooled by malicious attacks. Which relies on an effective selective sampling strategy to deal with the most favorite Videos? Some users, which we call spammers, post unrelated videos as responses to popular video topics. Online video sharing systems, out of which YouTube is the most popular, provide features that allow users to post a video as a response to a discussion topic. These features open opportunities for users to introduce polluted content, or simply pollution, into the system. So we find For instance, Spammers may post an unrelated video as response to a popular one, aiming at increasing the likelihood of the response being viewed by a larger number of users. Existing System: IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:[email protected]
- 2. Internet users post a large number of video clips on Video-sharing websites and social network applications every day. The video content may be duplicate, similar, related, or quite different. Facing billions of multimedia WebPages, online users are usually having a hard time finding their favorites. Some video-sharing websites recommend video lists for end users according to video classification, video description tags, or watching history. However, these recommendations are not accurate and are always not consistent with the end users’ interests. To improve this, some websites also provide users with search engine to search their desired videos quickly. However, searching is based on the keywords. Online Trading is being hosted on Stand Alone Server. Difficult to reuse video-tag module. Payment for combination of Physical Hosting and Hardware is demanded by the Web Hosting. Lack of scalability in Dedicated Servers. Difficult to identify the Spammers in online. Noise and inconsistencies inherent to the data, and illustrates the difficulty of the task. Provider on monthly basis, increasing total cost. Proposed System: Cloud-based mobile multimedia recommendation system which can reduce network overhead and speed up the recommendation process The users are classified into several groups according to their context types and values. With the accurate classification rules, the context details are not necessary to compute, and the huge network overhead is reduced. Moreover, user contexts, user relationships, and user profiles are collected from video-sharing websites to generate multimedia recommendation. That the proposed approach can recommend desired services with high precision, high recall, and low response delay. User clusters are collected instead of detailed user profiles. To avoid the explosion of network overhead, user-behavior- based clustering is performed first, and the collectors calculate user clusters according to the clustering rules and then report the user cluster to the recommender only.
- 3. Proposed tag-cloud recommendation approaches. A computing platform distributed in large-scale data center. Computing and storage resources. A search system ranked lists of top videos. Reusability and extensibility of this framework component. Private Storage space for each and every Provider. Detection video spammers and promoters Process is easy. A search system ranked lists of top videos. Reusability and extensibility of this framework component. Detecting users who disseminate video pollution, instead of classifying the content itself. Machine learning approach that explores the characteristics of pre classified users. Software Requirements: • Operating System : Windows Xp. • Platform : Java1.6, jsp, servlet. • Backend : MySQL5.0. • Server : Tomcat. Hardware Requirements: • Processor : Pentium IV Processor • RAM : 512 MB • Hard Drive : 40GB • Monitor : 14” VGA COLOR MONITOR • Disk Space : 1 GB