GeoSRS: A hybrid social recommender system for geolocated data. (April 2016)

• Record Type: Journal Article
• Authors: Capdevila, Joan; Arias, Marta; Arratia, Argimiro
• Is Part Of: Information systems. Volume 57(2016)
• Page Start: 111
• Page End: 128
• Publication Date: 2016-04
• Subjects: Recommender systems -- Text mining -- Quadtree -- Crawling -- Social networks -- Location-based social network; Database management -- Periodicals; Electronic data processing -- Periodicals; Bases de données -- Gestion -- Périodiques; Informatique -- Périodiques; Database management; Electronic data processing; Periodicals; 005.7
• DOI: 10.1016/j.is.2015.10.003
• Languages: English
• ISSNs: 0306-4379

Abstract

Abstract: We present GeoSRS, a hybrid recommender system for a popular location-based social network (LBSN), in which users are able to write short reviews on the places of interest they visit. Using state-of-the-art text mining techniques, our system recommends locations to users using as source the whole set of text reviews in addition to their geographical location. To evaluate our system, we have collected our own data sets by crawling the social network Foursquare . To do this efficiently, we propose the use of a parallel version of the Quadtree technique, which may be applicable to crawling/exploring other spatially distributed sources. Finally, we study the performance of GeoSRS on our collected data set and conclude that by combining sentiment analysis and text modeling, GeoSRS generates more accurate recommendations. The performance of the system improves as more reviews are available, which further motivates the use of large-scale crawling techniques such as the Quadtree .