How personalised online news recommendation helped a Belgian media group hit the headlines
When we met our client, a Belgian media group, they were at a crossroads. Their news business was undergoing rapid transformations and one of their challenges lied in poor digital adoption resulting in fewer subscribers, hence profit loss. In the meantime, their sports column was solid, driving most of their readership.
Personalised online news was most certainly the way to stand out. Yet, when such organisations embark upon implementing data-driven, algorithmic news personalisation services, they have to navigate a complex landscape. How to design it and implement it within their systems was a challenge we were eager to take.
When our client presented their challenges, it was very clear to us that their sports articles had to trigger readers’ loyalty.
So building algorithmic recommender systems on sports articles played a central role.
We kicked off this mission by extracting topics from any article using natural language processing. Thanks to frequently observed topics, our system was able to understand the meaning of topics and sort them out.
Then, we created a news recommender system based not only on users’ clicks and search but also on their individual favourite topics to make recommendation even more accurate.
For now, our model is available to a selection of beta testers. What is coming next is deploying this app to all the readers. From a news perspective, we see opportunities to perform media and social media scrapping. On the readers side,sorting them out based on real data is another project we’re working on.
Above the mission itself, we were thrilled to see how much digital transformation received positive reception within the teams of our client.