Big data visual exploration as a recommendation problem

Abstract

Big data visual exploration is believed to be considered as a recommendation problem. This proximity concerns essentially their purpose: it consists in selecting among huge amount of data those that are the most valuable according to specific criteria, to eventually present it to users. On the other hand, the recommendation systems are recently resolved mostly using neural networks (NNs). The present paper proposes three alternative solutions to improve the big data visual exploration based on recommendation using matrix factorisation (MF) namely: conventional, alternating least squares (ALS)-based and NN-based methods. It concerns generating the implicit data used to build recommendations, and providing the most valuable data patterns according to the user profiles. The first two solutions are developed using Apache Spark, while the third one was developed using TensorFlow2. A comparison based on results is done to show the most efficient one. The results show their applicability and effectiveness.