A recommender system suggests an item to a user that he/she may be interested in. To suggest an item of interest to a user, information from social networks is utilised to provide a suitable recommendation based on the user’s location. Different data databases are used to solve the location dimension problem. These data databases use small scale datasets to provide recommendation based on location, but in real time the volume of data is large. Analysis can be performed in two ways: qualitative and quantitative. Here, we analyse Foursquare data set qualitatively to study the need for big data in recommendation systems for location-based social networks (LBSN). A few quality parameters such as parallel processing and multimodal interface have been selected to study the need for big data in recommender systems. This paper gives a study and analysis of quality parameters of recommendation systems for LBSN with big data.
