Context Based Genuine Content Recommendation System Using Hadoop

In past, even though there is a lot of research work is done in the field of recommendation systems, the researchers did not target user contexts while recommending the content to the end users. Traditional recommendation systems while dealing with applications considers only users and items, and do not incorporate user context when delivering recommendations to querying end users. Contextual information can improve the quality of recommendation by overcoming the challenges in recommendation systems. Context-aware recommendation system (CARS) deals with various types of challenges in existing recommendation systems such as cold- start, sparsity, and scalability. One main challenge is to deliver genuine content to end users that need to be considered. Our work focuses on delivering the genuine content (video) recommendations based on user's context such as network type, time, location etc. The proposed work acts as a content filtering component that filters the content received from the existing system. This component can be applied to any existing recommendation system for improving its content genuinity. The work is implemented on Hadoop, an open source software for scalable, distributed computing.