Preserving privacy in location-based mobile social applications
Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications
Temporal recommendation on graphs via long- and short-term preference fusion
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Preserving location and absence privacy in geo-social networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A generalized taxonomy of explanations styles for traditional and social recommender systems
Data Mining and Knowledge Discovery
Proceedings of the VLDB Endowment
Proceedings of the First ACM SIGSPATIAL International Workshop on Mobile Geographic Information Systems
Time-aware point-of-interest recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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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.).