An efficient and scalable location sensing recommendation - Sreedevi, M.;Narasimha, C.
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An efficient and scalable location sensing recommendation is used to generate recommendations based on location-based ratings. The existing recommendation systems follow the traditional way to generate recommendations for user which does not consider the spatial properties. On contrary the proposed implementation location sensing recommendation offers three types of location-based ratings 1) spatial ratings for non-spatial items 2) non-spatial ratings for spatial items and 3) spatial ratings for spatial items. The proposed system follows a technique of user partitioning technique which…mehr

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Produktbeschreibung
An efficient and scalable location sensing recommendation is used to generate recommendations based on location-based ratings. The existing recommendation systems follow the traditional way to generate recommendations for user which does not consider the spatial properties. On contrary the proposed implementation location sensing recommendation offers three types of location-based ratings 1) spatial ratings for non-spatial items 2) non-spatial ratings for spatial items and 3) spatial ratings for spatial items. The proposed system follows a technique of user partitioning technique which influences the proposed recommendations along with the rating spatially close to the location of querying users in the way of maximizing the system scalability without sacrificing quality of the recommendations.
Autorenporträt
Dr.M. Sreedevi completed Ph.D from SV University, tirupati, Presently working as head of the department, CSE, Madanapalle Institute of technology & Science, Madanapalle, Chittoor, Andhra Pradesh, research interest is computer networksMr.C.Narasimha , completed M.Tech, and presently working as Assistant Professor in dept of CSE, MITS, Madanapalle.