Conclusion
Till date, recommender systems make recommendations only by getting data from data warehouses. Due to the dynamic nature of the recommender system, the data have to be distributed. The recommender system has to work parallelly to provide recommendation to the user and to support different interfaces. Big data excels in handling unstructured, raw and complex data with huge programming flexibility. This study analyses the use of big data in recommendation systems qualitatively. In future research, we will attempt to analyse the recommendation system in social networks quantitatively.
