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Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media

Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
Author to whom correspondence should be addressed.
Academic Editors: Georg Gartner, Haosheng Huang and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2016, 5(12), 245;
Received: 9 August 2016 / Revised: 23 October 2016 / Accepted: 7 November 2016 / Published: 19 December 2016
(This article belongs to the Special Issue Location-Based Services)
PDF [6220 KB, uploaded 20 December 2016]


Advances in location acquisition and mobile technologies led to the addition of the location dimension to Social Networks (SNs) and to the emergence of a newer class called Location-Based Social Networks (LBSNs). While LBSNs are richer in their model and functions than SNs, they fail so far to attract as many users as SNs. On the other hand, SNs have large amounts of geo-tagged media that are under-utilized. In this paper, we propose an Interest-Aware Location-Based Recommender system (IALBR), which combines the advantages of both LBSNs and SNs, in order to provide interest-aware location-based recommendations. This recommender system is proposed as an extension to LBSNs. It is novel in: (1) utilizing the geo-content in both LBSNs and SNs; (2) ranking the recommendations based on a novel scoring method that maps to the user interests. It also works for passive users who are not active content contributors to the LBSN. This feature is critical to increase the number of LBSN users. Moreover, it helps with reducing the cold start problem, which is a common problem facing the new users of recommender systems who get random unsatisfying recommendations. This is due to the lack of user interest awareness, which is reliant on user history in most of the recommenders. We evaluated our system with a large-scale real dataset collected from foursquare with respect to precision, recall and the f-measure. We also compared the results with a ground truth system using metrics like the normalized discounted cumulative gain and the mean absolute error. The results confirm that the proposed IALBR generates more efficient recommendations than baselines in terms of interest awareness. View Full-Text
Keywords: location-based social networks; geo-tagged media; location-based services; user interests; recommender systems location-based social networks; geo-tagged media; location-based services; user interests; recommender systems

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AlBanna, B.; Sakr, M.; Moussa, S.; Moawad, I. Interest Aware Location-Based Recommender System Using Geo-Tagged Social Media. ISPRS Int. J. Geo-Inf. 2016, 5, 245.

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