A study and analysis of recommendation systems for location-based social network (LBSN) with big data

Abstract

Recommender systems play an important role in our day-to-day life. A recommender system automatically suggests an item to a user that he/she might be interested in. Small-scale datasets are used to provide recommendations based on location, but in real time, the volume of data is large. We have selected Foursquare dataset to study the need for big data in recommendation systems for location-based social network (LBSN). A few quality parameters like parallel processing and multimodal interface have been selected to study the need for big data in recommender systems. This paper provides a study and analysis of quality parameters of recommendation systems for LBSN with big data.