info:eu-repo/semantics/article
On the impact of neighborhood selection strategies for recommender systems in LBSNs
Fecha
2017-08Registro en:
Rios, Carlos; Schiaffino, Silvia Noemi; Godoy, Daniela Lis; On the impact of neighborhood selection strategies for recommender systems in LBSNs; Springer; Lecture Notes in Computer Science; 10061 LNAI; 8-2017; 196-207
0302-9743
CONICET Digital
CONICET
Autor
Rios, Carlos
Schiaffino, Silvia Noemi
Godoy, Daniela Lis
Resumen
Location-based social networks (LBSNs) have emerged as a new concept in online social media, due to the widespread adoption of mobile devices and location-based services. LBSNs leverage technologies such as GPS, Web 2.0 and smartphones to allow users to share their locations (check-ins), search for places of interest or POIs (Point of Interest), look for discounts, comment about specific places, connect with friends and find the ones who are near a specific location. To take advantage of the information that users share in these networks, Location-based Recommender Systems (LBRSs) generate suggestions based on the application of different recommendation techniques, being collaborative filtering (CF) one of the most traditional ones. In this article we analyze different strategies for selecting neighbors in the classic CF approach, considering information contained in the users’ social network, common visits, and place of residence as influential factors. The proposed approaches were evaluated using data from a popular location based social network, showing improvements over the classic collaborative filtering approach.