Szczegóły publikacji
Opis bibliograficzny
Real-time recommendation system for stock investment decisions / Artur Bugaj, Weronika T. ADRIAN // W: WEBIST 2021 [Dokument elektroniczny] : 17th international conference on Web Information Systems and Technologies : October 26-28, 2021, [online] : proceedings / eds. Francisco Domínguez Mayo, Massimo Marchiori, Joaquim Filipe. — Wersja do Windows. — Dane tekstowe. — [Setúbal : SCITEPRESS – Science and Technology Publications, Lda], [cop. 2021]. — (WEBIST ; ISSN 2184-3252). — e-ISBN: 978-989-758-536-4. — S. 490–493. — Wymagania systemowe: Adobe Reader. — Tryb dostępu: https://www.scitepress.org/Link.aspx?doi=10.5220/001071490000... [2022-02-02]. — Bibliogr. s. 493, Abstr. — Dostęp do pełnego tekstu po zalogowaniu
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 138972 |
|---|---|
| Data dodania do BaDAP | 2022-02-02 |
| DOI | 10.5220/0010714900003058 |
| Rok publikacji | 2021 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Konferencja | 17th International Conference on Web Information Systems and Technologies |
| Czasopismo/seria | WEBIST (Setúbal) |
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.