M. S. Kahil, A. Bouramoul and M. Derdour. 2023. Big data visual exploration as a recommendation problem. Int. J. Data Min. Model. Manag., 15, 2 (June 2023), Inderscience Publishers Ltd, 133-153. DOI: https://doi.org/10.1504/IJDMMM.2023.131378.

Kahil, M. S., Bouramoul, A. & Derdour, M. (2023, June). Big data visual exploration as a recommendation problem. Int. J. Data Min. Model. Manag., 15(2), Inderscience Publishers Ltd, 133-153. DOI: https://doi.org/10.1504/IJDMMM.2023.131378

M. S. Kahil, A. Bouramoul and M. Derdour, "Big data visual exploration as a recommendation problem". Int. J. Data Min. Model. Manag., vol. 15, no. 2, Inderscience Publishers Ltd, pp. 133-153, June, 2023. DOI: https://doi.org/10.1504/IJDMMM.2023.131378.

@article{misc-lab-430,
author = {Kahil, Moustafa Sadek and Bouramoul, Abdelkrim and Derdour, Makhlouf},
title = {Big data visual exploration as a recommendation problem},
journal = {Int. J. Data Min. Model. Manag.},
volume = {15},
number = {2},
issn = {1759-1163},
pages = {133--153},
publisher = {Inderscience Publishers Ltd},
year = {2023},
month = {June},
doi = {10.1504/IJDMMM.2023.131378},
url = {https://doi.org/10.1504/IJDMMM.2023.131378},
keywords = {big data visualisation, recommendation systems, collaborative filtering, content-based filtering, matrix factorisation, alternating least square, machine learning, neural networks.}
}

TI  - Big data visual exploration as a recommendation problem
AU  - M. S. Kahil
AU  - A. Bouramoul
AU  - M. Derdour
PY  - 2023
SN  - 1759-1163
JO  - Int. J. Data Min. Model. Manag.
VL  - 15
IS  - 2
SP  - 133
EP  - 153
PB  - Inderscience Publishers Ltd
AB  - 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.
KW  - big data visualisation
KW  - recommendation systems
KW  - collaborative filtering
KW  - content-based filtering
KW  - matrix factorisation
KW  - alternating least square
KW  - machine learning
KW  - neural networks.
DO  - 10.1504/IJDMMM.2023.131378
UR  - https://doi.org/10.1504/IJDMMM.2023.131378
ID  - misc-lab-430
ER  -