Recommendations in location-based social networks: a survey

563 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Trajectory Data Mining (2015, background): “representing human mobility can help build a better social network [Bao et al. 2015; Zheng ACM Transactions on Intelligent Systems and Technology, Vol. 6, No. 3, Article 29, Publication date: May 2015.”
Deep reinforcement learning for page-wise recommendations (2018, background): “Recommender systems have become increasingly popular in recent years, and have been utilized in a variety of domains including movies, music, locations, and social events [2, 11, 13, 30, 31, 39, 41, 45].”
A Big Data Analytics Method for Tourist Behaviour Analysis (2017, unknown): “[2] provided a comprehensive survey of TRS that use location-based data in social networks, but do not relate any of these to support strategic decision-making by DMOs.”
Location based services: ongoing evolution and research agenda (2018, background): “…by companies, many research studies utilise the user-generated contents of these applications for making (location) recommendations (Huang 2016; Bao et al. 2015), modelling individual and crowd mobility, understanding the dynamics and semantics of cities (Gao, Janowicz, and Couclelis 2017) and…”
PrivCheck: privacy-preserving check-in data publishing for personalized location based services (2016, background): “In particular, Location Based Social Networks (LBSNs), such as Foursquare 1 , have become primary data sources for developing personalized LBSs [3, 45, 42].”