APRA: An approximate parallel recommendation algorithm for Big Data

In this paper, we propose a new solution based on Spark, which is tailored to handle large-scale data and provide better results. Particularly, we take advantage of the in-memory operations available through Spark, to improve the performance of recommendation systems in context of Big Data. Experimental results on two real-world data sets confirm the claim.