On the impact of neighborhood selection strategies for recommender systems in LBSN

CARLOS RIOS; SILVIA SCHIAFFINO; DANIELA GODOY,

Cancún

Conferencia; 15th Mexican International Conference on Artificial Intelligence, MICAI 2016; 2016

SMIA

Location-based social networks (LBSNs) have emerged as anew concept in online social media, due to the widespread adoption ofmobile devices and location-based services. LBSNs leverage technologiessuch 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 friendsand find the ones who are near a specific location. To take advantage ofthe information that users share in these networks, Location-based RecommenderSystems (LBRSs) generate suggestions based on the applicationof different recommendation techniques, being collaborative filtering(CF) one of the most traditional ones. In this article we analyze differentstrategies for selecting neighbors in the classic CF approach, consideringinformation contained in the users? social network, common visits,and place of residence as influential factors. The proposed approacheswere evaluated using data from a popular location based social network,showing improvements over the classic collaborative filtering approach.