Abstract:
Location Based Social Networks (LSBNs) has started with broadcasting the location via text messages long before the mobile applications and mobile data. Due to the technological developments, a notable number of technologies we use today are location-aware, and they are available to more people. When one of the most popular LSBN, Foursquare, has brought together both motivation of gameplay and social networks, and it introduced a new business model to the market. They have used points and badges to motivate users for “checking in” to locations. After that point, LSBNs started to be a useful tool that effected purchase decision and became vital for information research, evaluation of alternatives and post-purchase evaluation. Typically, the score of the venues is a significant decision-making parameter for most of the users for a purchase decision. Due to the complex and undisclosed score calculation method of Foursquare, it has been a wonder to users and venue owners. Purpose of the research is to build a model that can predict venue scores based on variables such as check-in counts, review, tip and photo counts of venues.
