SJORS: A Semantic Recommender System for Journalists

Garrido, Ángel Luis ; Pera, Maria Soledad ; Bobed, Carlos (Universidad de Zaragoza)

Resumen: Recommender Systems support a broad range of domains, each with peculiarities that recommendation algorithms must consider to produce appropriate suggestions. In the paper, we bring attention to a little-studied scenario related to the news domain: recommendations catering to media journalists. Based on the particular needs inherent to a newsroom, the authors introduce SJORS, a wire news Recommender System that takes into account the activities of each journalist as well as other critical factors that arise in this particular domain, such as wire news recency. Given the nature of the items recommended, SJORS deals with the inherent ambiguity of natural language by exploiting different semantic techniques and technologies. The authors have conducted several experiments in a media company, which validated the performance and applicability of the system. Outcomes emerging from this work could be extended to other domains of interest, such as online stores, streaming platforms, or digital libraries, to name a few.

Idioma: Inglés

DOI: 10.1007/s12599-023-00843-6

Año: 2023

Publicado en: Business & Information Systems Engineering (2023), [18 pp.]
ISSN: 2363-7005

Factor impacto JCR: 7.4 (2023)

Categ. JCR: COMPUTER SCIENCE, INFORMATION SYSTEMS rank: 14 / 249 = 0.056 (2023) - Q1 - T1

Factor impacto CITESCORE: 13.6 - Information Systems (Q1)

Factor impacto SCIMAGO: 1.611 - Information Systems (Q1)

Financiación: info:eu-repo/grantAgreement/ES/AEI/PID2020-113903RB-I00

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Área Lenguajes y Sistemas Inf. (Dpto. Informát.Ingenie.Sistms.)