SJORS: A Semantic Recommender System for Journalists

Ángel Luis Garrido*, Maria Soledad Pera, Carlos Bobed

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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.

Original languageEnglish
Number of pages18
JournalBusiness and Information Systems Engineering
Publication statusPublished - 2023


  • Journalists
  • Machine learning
  • NLP
  • Recommender systems
  • Semantics


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