This could include recommendations for restaurants, museums, or other points of interest or events near the user's location.
These services take advantage of the increasing use of smartphones that store and provide the location information of their users alongside location-based social networks (LBSN), like Foursquare, Gowalla, Swarm, and Yelp.
In addition to geosocial networking services, traditional online social networks such as Facebook and Twitter are using the location information of their users to show and recommend upcoming events, posts, and local trends.
In addition to its value for users, this information is valuable for third-party companies to advertise products, hotels, places, and to forecast service demand such as the number of taxis needed in a part of a city.
These systems have become increasingly popular and are used for movies, music, news, books, research articles, search queries, social tags, and products in general.
This type of recommendation is quite valuable, especially for those who are traveling to a new city and want the best experience during their trip.
The implicit goal of this type of recommendation is to lift the user's burden of searching for an interesting place.
Researchers at a 2010 Institute of Electrical and Electronics Engineers (IEEE) conference discussed the need of a reliable fine-grained dataset of previous user-attendance in order to provide social-event detection.