Abstract
Location aware recommender system (LARS) usesthe location based rating to provide recommendations. Traditionally, many recommended systems are very poor inproviding proper spatial details to its users especially forproducts and items, but LARS has specialized feature ofaccuracy in predicting specific locations on basis of rating. Thistechnique exploits spatial rating destination closest to its users. LARS use three types of location or destination based ratingslike – non-specific spatial location rating for specificallylocated spatial items, specifically located specific spatial ratingfor non-specific spatial items and specifically located spatialrating for specifically located spatial item. With the help ofLars, user rating location as well as the item locations can beexploited. User location exploits by user partition processwhich in eases recommendations with online modelling as wellas offline modelling. Item locations are executed by usingtravel penalty procedure which favours recommendationswhich is closer to the user and user's location. Travel penaltyprocedure or a querying user executed on together or independently.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
