TY - JOUR
T1 - SJORS
T2 - A Semantic Recommender System for Journalists
AU - Garrido, Ángel Luis
AU - Pera, Maria Soledad
AU - Bobed, Carlos
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Journalists
KW - Machine learning
KW - NLP
KW - Recommender systems
KW - Semantics
UR - http://www.scopus.com/inward/record.url?scp=85180240956&partnerID=8YFLogxK
U2 - 10.1007/s12599-023-00843-6
DO - 10.1007/s12599-023-00843-6
M3 - Article
AN - SCOPUS:85180240956
SN - 2363-7005
JO - Business and Information Systems Engineering
JF - Business and Information Systems Engineering
ER -