Using an Explicit Query and a Topic Model for Scientific Article Recommendation

Autor/inn/en	Smail, Boussaadi; Aliane, Hassina; Abdeldjalil, Ouahabi
Titel	Using an Explicit Query and a Topic Model for Scientific Article Recommendation
Quelle	In: Education and Information Technologies, 28 (2023) 12, S.15657-15670 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Smail, Boussaadi)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-023-11817-2
Schlagwörter	Scientific and Technical Information; Journal Articles; Online Searching; Databases; Artificial Intelligence; Semantics; Algorithms + Suchen Sie Ihr Suchwort? Journal article; Zeitschriftenaufsatz; Online-Recherche; Datenbank; Künstliche Intelligenz; Semantik; Algorithm; Algorithmus
Abstract	The search for relevant scientific articles is a crucial step in any research project. However, the vast number of articles published and available online in digital databases (Google Scholar, Semantic Scholar, etc.) can make this task tedious and negatively impact a researcher's productivity. This article proposes a new method of recommending scientific articles that takes advantage of content-based filtering. The challenge is to target relevant information that meets a researcher's needs, regardless of their research domain. Our recommendation method is based on semantic exploration using latent factors. Our goal is to achieve an optimal topic model that will serve as the basis for the recommendation process. Our experiences confirm our performance expectations, showing relevance and objectivity in the results. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01