A scalable model for real-time venue recommendations using MapReduce

The popular social networks such as Facebook, Twitter, and Foursquare closely monitor user activities to recommend different services and events. Among others, venue recommendation proposes users the most appropriates venues based on the user preferences. It offers facility to the user to mark the check-ins when a venue is visited. The traditional venue recommendation systems have opted collaborative filtering to propose recommendations. However, collaborative filtering overlooked certain critical issues, including real-time recommendations, cold start, and scalability, for venue recommendations. Moreover, real-time physical factors such as distance from the venue are also not considered in traditional venue recommendation systems. Furthermore, parsing and processing of huge volume of unstructured data is the main challenge for conventional recommender systems, particularly when dealing with real-time recommendations. For efficient scaling, significant computational and storage resources for recommendation systems are desired. This article proposes a Real-Time Venue Recommendation (RTVR) model that utilizes cloud-based MapReduce framework to process, compare, mine, and manage large data sets for generating recommendations. The results showed that the proposed model has improved accuracy for real-time recommendations. The proposed RTVR is more scalable as it exploits a cloud-based architecture. Moreover, the proposed techniques are verified using formal verification methods.