Recommending News in Traditional Media Companies
| العنوان: | Recommending News in Traditional Media Companies |
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| المؤلفون: | Jon Gulla, Rolf Svendsen, Lemei Zhang, Agnes Stenbom, Jørgen Frøland |
| المصدر: | AI Magazine; Vol. 42 No. 3: Fall 2021; 55-69 |
| بيانات النشر: | Association for the Advancement of Artificial Intelligence, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Artificial Intelligence |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2371-9621 0738-4602 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d82f4a2a02836748d250180751aff5c0 https://ojs.aaai.org/index.php/aimagazine/article/view/18146 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....d82f4a2a02836748d250180751aff5c0 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 23719621 07384602 |
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