New perspectives for recommendations in location-based social networks: Time, privacy and explainability
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Online social networks have attracted users' attention in the last decade. Recommendation services constitute a critical functionality of such social platforms: users receive recommendations about resources (documents, pieces of music) and potential friends (people with the same interests). Recently, technological progressions in smart phones enabled the exploitation of geographical data information in social networks. Users can now receive recommendations about new Points of Interest (POIs), and new activities in POIs. Eventually, Location-based Social Networks (LBSNs) may become the 'Next Big Thing' of the Internet industry. This paper surveys the related work and current state-of-the-art algorithms in LBSNs. We also provide three new perspectives that concern recommendations in LBSNs: time-awareness, user's privacy issues, and explainability of recommendations. We present the latest work in LBSNs by comparing real systems and by categorizing them in multiple ways (platforms, personalization, etc.). © 2013 Authors.
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Symeonidis P; Krinis A; Manolopoulos Y (Springer, 2013)Social networks have evolved with the combination of geographical data, into location-based social networks (LBSNs). LBSNs give users the opportunity, not only to communicate with each other, but also to share images, ...
Symeonidis P; Ntempos D; Manolopoulos Y (Springer New York, 2014)Online social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, ...
Sattari M; Toroslu I; Karagoz P; Symeonidis P; Manolopoulos Y (Springer-Verlag London Ltd, 2015)With the increasing availability of location-based services, location-based social networks and smart phones, standard rating schema of recommender systems that involve user and item dimensions is extended to three-dimensional ...