Recommendation Systems: A Deep Learning Oriented Perspective

Igor Lampa, Vitoria Gomes, Geraldo Zafalon

2024

Abstract

The massive use of the digital platforms has provided an exponential increase at the amount of data consumed and daily generated. Thus, there is a data overload which directly affects the consume experience of digital products, whether at find a news, consume an e-commerce product or to choose a movie in a streaming platform. In this context, emerge the recommendation systems, which have the finality of provide an efficient way to comprehend the user predilections and to recommend direct items. Thus, this work brings the classical concepts and techniques already used, as well as analyzes their use along with deep learning, which through evaluated results has a grater capability to obtain implicit relationships between users and items, providing recommendations with better quality and accuracy. Furthermore, considering the review of the literature and analysis provided, an architectural model for recommendation system based on deep learning is proposed, which is defined as a hybrid system.

Paper Citation

in Harvard Style

Lampa I., Gomes V. and Zafalon G. (2024). Recommendation Systems: A Deep Learning Oriented Perspective. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 682-689. DOI: 10.5220/0012622700003690

in Bibtex Style

@conference{iceis24,
author={Igor Lampa and Vitoria Gomes and Geraldo Zafalon},
title={Recommendation Systems: A Deep Learning Oriented Perspective},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={682-689},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012622700003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Recommendation Systems: A Deep Learning Oriented Perspective
SN - 978-989-758-692-7
AU - Lampa I.
AU - Gomes V.
AU - Zafalon G.
PY - 2024
SP - 682
EP - 689
DO - 10.5220/0012622700003690
PB - SciTePress