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Research Article Open Access
There are various ways and methods used in video recommendation which are purely statistical. These would give recommendations to users based on either their previous search or other criteria. These systems set up a large number of context collectors at the terminals. However, the context collecting and exchanging result in heavy network overhead, and the context processing consumes huge computation. Due to these criterion users end up getting unnecessary content which makes the browser slow. In this paper we propose a user specific category based promotion, we propose and provide for characterization of individual content as well as social attributes that help distinguish each user class. Thus a user defined video recommendation would ensure faster access to only important information which is in the user's domain of interest which utilises low buffer space and increase the speed of the system for user satisfaction.
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Author(s): A.Deva Varaprasad , N.J.Subashini , Shepard Chifamba