Cloud Based Mobile Video Recommendation System with User Behaviour
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 9
Abstract
Nowadays, Countless online video services are available Users usually waste lot of time to obtain their interested videos in browsing and watching those videos on mobile. The existing result shows the poor service quality of video streaming over mobile network such long buffering and interrupt happen in the streaming video. The proposed system is a video recommendation system which can speed up the recommendation process and reduce network overhead. The mobile video streamed by using the cloud. For video-sharing mobile application Mobile properties are collected for context aware recommendation. The Cloud based recommendation system is created with Mahout Machine learning library. Core algorithms of Mahout are used for classification, cluster and collaborative Moreover filtering. We use Mahout’s user and item based recommendation and user behaviour. The System collects user, item, rating, genre and viewing time for creating clusters of user profiles to generate implicit preferences video recommendations. Mobile properties such as location, time and network types are used to generate recommendation rules. User context and profile clusters are used for finding video recommendation from the massive amount of video collection on cloud. The proposed system can recommend desired services with high recall, high precision and low response delay.
Authors and Affiliations
Ms. Latika R. Gaddam, Mrs. Pratibha S. Yalagi
Keywords
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