The revealing of advancements in mobile technologies and wide application of location based services have derived the emergence of location based services considering user similarities. The demand for location based systems integration with recommendation systems is extracted as a need of understanding the process of customer preferences considering location, time and actual needs. The needs can be derived from consumer life style, demographical information, consumption behavior and reaction to previously sent messages. On the other hand, these factors don't solely reflect the final purchasing decision which can cause imprecise environment for recommendation systems. Thus, researchers try to search other indicators that can reflect customer characteristics such as geographical data, digital participation in social media and search history of products for better understanding of the changes in customers purchasing tendency. Thus, in this paper, an intuitionistic fuzzy set theory based recommendation system is constructed by integrating three widely used social platforms: Trip Advisor, Zomato and Foursquare to implement restaurant offerings to proper social platform users. First, a sentiment analysis is adapted to selected restaurants and number of negative, positive and neutral comments are gathered. After that, restaurant and location information are emerged according to several criteria and user and location clustering are adapted separately via fuzzy clustering. Finally, multi criteria interval valued intuitionistic fuzzy recommendation system is adapted for restaurant recommendations to similar customer groups.