Real-time Recommendation System for Stock Investment Decisions

Artur Bugaj, Weronika Adrian

2021

Abstract

Recommendation systems have become omnipresent, helping people making decisions in various areas. While most of the systems can give accurate recommendations, their learning procedures can be time-consuming. In some cases, this is not permissible; for example when the information about the items and users changes very fast in time. In this paper, we discuss a new recommendation engine, based on labelled property graph knowledge representation and attributed network embeddings, which calculates real-time recommendations for stock investment decisions. In particular, we demonstrate an application of the DANE (dynamic attributed network embedding) framework proposed by Li et al. and show the promising results of the system.

Paper Citation

in Harvard Style

Bugaj A. and Adrian W. (2021). Real-time Recommendation System for Stock Investment Decisions. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-536-4, pages 490-493. DOI: 10.5220/0010714900003058

in Bibtex Style

@conference{webist21,
author={Artur Bugaj and Weronika Adrian},
title={Real-time Recommendation System for Stock Investment Decisions},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2021},
pages={490-493},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010714900003058},
isbn={978-989-758-536-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Real-time Recommendation System for Stock Investment Decisions
SN - 978-989-758-536-4
AU - Bugaj A.
AU - Adrian W.
PY - 2021
SP - 490
EP - 493
DO - 10.5220/0010714900003058