Improved Context-aware YouTube Recommender System with User Feedback Analysis
YouTube is one of the most popular video sharing website being used by the users throughout the world. For providing ease to the user it offers a list of recommended videos every time the user searches some content. But many times the provided or recommended videos are not related to context that the user had searched. This is due to the title and the description of the videos which are although related to the keyword that the user had searched but the content of the video may be different. Moreover, the videos are recommended on the premise of users’ interest irrespective of the context they are in. Therefore, the recommended videos cover different interests of the user altogether. The existing approaches are predominantly based on content and collaborative recommendations. So in this research work, the proposed and recommended approach is context based. The recommended videos are to be positioned on the basis of association and comment feedback. Moreover, for improving the quality of ranking, structural analysis (i.e. Meta information about the videos) is also performed on each video to get high relativity videos.