Abstract: Location based Video Searching (LBVS) systems have been shown as valuable tools for providing appropriate recommendations to users. In the last decade, the amount of customers, services and online information has grown rapidly. Moreover, most of existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements. We will develop Smart (LBVS), to address the above challenges. It aims at presenting a personalized service recommendation list and recommending the most appropriate services to the users effectively. Specifically, keywords are used to indicate users' preferences, and a user-based Collaborative and Demographic Filtering algorithm is adopted to generate appropriate recommendations. Extensive experiments are conducted on real-world data sets, and results demonstrate that Smart Recommendation System significantly improves the accuracy and scalability of service recommender systems over existing approaches. Geo-location is a feature that is presented individually to users. The users can share location and add geo-located data to content. Adding geographical identification metadata to an information resource attach value to the content by making it more searchable.
Keywords: Location based Video Searching, customer reviews,Geo-location, metadata.