Recommending News in Traditional Media Companies


  • Jon Atle Gulla Norwegian University of Science and Technology
  • Rolf Dyrnes Svendsen NxtMedia Lab
  • Lemei Zhang Norwegian University of Science and Technology
  • Agnes Stenbom Schibsted
  • Jørgen Frøland Polaris Media



The adoption of recommender systems in online news personalization has made it possible to tailor the news stream to the individual interests of each reader. Previous research on commercial recommender systems has emphasized their use in large-scale media houses and technology companies, and real-world experiments indicate substantial improvements of click rates and user satisfaction. It is less understood how smaller media houses are coping with this new technology, how the technology affects their business models, their editorial processes, and their news production in general. Here we report on the experiences from numerous Scandinavian media houses that have experimented with various recommender strategies and streamlined their news production to provide personalized news experiences. In addition to influencing the content and style of news stories and the working environment of journalists, the news recommender systems have been part of a profound digital transformation of the whole media industry. Interestingly, many media houses have found it undesirable to automate the entire recommendation process and look for approaches that combine automatic recommendations with editorial choices.




How to Cite

Gulla, J., Svendsen, R. ., Zhang, L., Stenbom, A., & Frøland, J. (2021). Recommending News in Traditional Media Companies. AI Magazine, 42(3), 55-69.



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