Personalisation is a developing domain in news publishing. 67% of publishers are already using AI personalisation recommenders, but what’s next? A recent research paper from the KU Leuven brings a new perspective in the interaction between publishers, readers and algorithms. The paper “Putting a Human Face on the Algorithm: Co-Designing Recommender Personae to Democratize News Recommender Systems” details the concept of news recommender personae. Twipe’s Riccardo Fredro talked to the authors of the paper, Lawrence Van den Bogaert and Jaron Harambam to understand why they think this can play a role in the future of news. Watch the full conversation here.
AI news personae use multiple algorithms per user to tailor recommendations on a 1-to-1 basis for different reading situations. When visiting a website, readers get presented with the possibility to read 3 different curated feeds rather than 1. The approach is explained in more detail in the video below.
To create the news personae, the research team studied the news consumption habits of a control group of 15 news readers. Each member kept a diary, noting down which articles they had read and providing a reflection of 1 article per week. From this data, the researchers distilled a set of core reading motivations which were made into 8 persona.
These 8 persona were presented to a new control group of 12 people with a fresh perspective who were asked to fit each persona to a specific news story. 8 persona then became 3, with the decision made that people shouldn’t have to identify with personae on a 1 to 1 level. The final personae were:
Each persona was then given a visual identity to add to the transparency of the algorithms and make them approachable. The news personae algorithms give the end user greater agency around the recommendations that they receive. Different persona can be chosen at different points in the day to match their reading mood and needs. Aside from the 3 personae, readers can also still access the news without any element of personalisation.
Publishers have adopted forms of personae in product development to engage new audiences. When designing l’Édition du Soir, Ouest-France focused on the persona of the unwinder. The evening edition product was designed after studying an evening increase in user behaviour and noticing peaks on content like quizzes and games. L’Édition du Soir was created with these readers and their interests at heart, including videos, games, puzzles and also short stories about the main news. The New York Times have also leveraged this persona with their bundle products offering readers more than just the news.
The expert persona has been leveraged in niche publications. The Athletic have successfully adopted this model in their reinvention of local sports coverage. Hiring specific reporters and data experts to focus on specific teams and sports, they have popularised the long-read and deep dive concepts for sports readers. Their coverage is designed to bring sports fans closer to the heart of the game. Ahead of 2022, The Athletic even launched an ”emergency football hotline” to provide fans with expert opinions.
Back in 2018, The Guardian experimented with a commuter persona through their app LabRDR. LabRDR was an offline experiment where users got ‘packages’ of Guardian articles delivered to their phones twice a day. These packages were sent at times they determined to fit their commute. The packages were tailored to the length of consumer’s commutes and were personalised to provide a set of stories the commuter would want to read. March 2018 was the last time the app was updated, with the product then discontinued.
JAMES news recommendations are leveraged by publishers like NRC and Keskisuomalainen and act as a form of “explorer” persona. The personalised recommendations use a hybrid algorithm to combine trending news with personalised interest areas. In doing so, readers are typically exposed to 80-90% of the day’s news content, being given a broader depth of news.
Whilst the news personae algorithm concept is not yet implemented, it does stimulate certain discussion points for product and data teams. We’ve listed a selection of ours below:
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