Today’s news consumers expect personalized content recommendations. This personalization is based on algorithms that predict what a user is most likely to click on, and often tends to generate a filter bubble: an endless loop of similar recommendations. To overcome this challenge, the NewsButler project will investigate and create a smart content stream recommender and editorial optimization engine that helps readers and editorial teams create a personalized, user-controlled news experience.
Tech giants have rich means to develop highly personalized digital tools. However, their broad approach to content curation can lead to widespread noise overload and disinformation. Publishers offering high-quality content must develop their own media consumption platforms that can compete by meeting the needs of demanding consumers in terms of both pricing and personalization.
Harnessing the expertise of companies active in news product development, machine learning and human-computer interaction, the NewsButler consortium will further develop high-potential content personalization tools and combining them with human and machine intelligence. This holistic project will also explore new business and monetization models.
Outcomes will benefit three stakeholder groups: content readers, content editors and publishers. The NewsButler project will result in:
In addition to the NewsButler content recommendation engine, the project will also culminate in an editorial dashboard that analyzes and suggests content streams. It will also develop an intelligent newsbot that interfaces between the engine and the users. These outcomes drive diverse, high-quality news offerings, avoids filter bubbles and poses a sustainable alternative to the “fake news” phenomenon.
“NewsButler will create a smart content stream recommender and editorial optimization engine combining human and machine intelligence that helps readers and editorial teams create a personalized, user-controlled news experience.”