Abstract
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.
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Acknowledgements
This work has been supported by ANPCyT PICT No. 2011-0366, CONICET PIP No. 11220150100030CO (2015–2017) and PIP 112-201101-00078 (2012–2015).
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Ríos, C., Schiaffino, S., Godoy, D. (2017). On the Impact of Neighborhood Selection Strategies for Recommender Systems in LBSNs. In: Sidorov, G., Herrera-Alcántara, O. (eds) Advances in Computational Intelligence. MICAI 2016. Lecture Notes in Computer Science(), vol 10061. Springer, Cham. https://doi.org/10.1007/978-3-319-62434-1_16
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DOI: https://doi.org/10.1007/978-3-319-62434-1_16
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