The exponential growth of digital information in the recent decades often referred to Big Data, poses new challenges for the current RS. What relevant research is going on to solve this problem?
Big data have inspired many researchers to improve the performance of recommender systems for handling large-scale datasets. Some relevant research in this area can be mentioned as follows. (Hammou, et al., 2019) used matrix factorization and random forest on big data recommendation systems that focuses on dividing the big data problem into smaller subproblems to speed up the process. (Verma, et al., 2015) explained how to use a recommendation system with big data. The paper proposed using CF, CBF, and hybrid filtering on Hadoop framework. Their result showed that with properly applied algorithms, the program's running time only increases slightly while the amount of the data is grown significantly. (Maximilian, et al., 2018) evaluated the effect of Big Data on recommendation quality by experimenting with the performance of a regression model on the example of Internet Search.
