A promising European solution for the reliable, secure and personalized consumption of news content
Every second, we are flooded with new information from various media. At the same time, it is becoming increasingly difficult to keep track, to distinguish fake news from reliable sources, and to always have the latest updates at hand. Fortunately, there is already a promising solution. Newsadoo, a Linz-based company and one of Europe’s most promising media start-ups, has been addressing these challenges since its founding in 2017.
The Newsadoo product – often referred to as the Spotify for news – enables users to access numerous sources and consume content seamlessly across devices. Whether on desktop, mobile, or voice assistants, the application ensures a continuous reading experience.
At the core lies an intelligent algorithm. It learns from user behavior and identifies correlations between time, situation, device, and content. Thanks to this personalization, users receive relevant and engaging articles whenever they need them. The system suggests content via self-learning models, also known as a recommender system.
For the suggestion mechanism to deliver high-quality results, the system requires more than recommendations alone. The underlying evaluation of articles also plays a crucial role. First, the texts must be processed and analyzed automatically. Techniques from Natural Language Processing (NLP) transform unstructured text into structured, analyzable content.
Together with Newsadoo and SCCH, RISC Software GmbH is advancing the recommendation algorithm in the TIDE project (Transparent, Intelligent, Diverse, European News Recommendation Algorithm). The research introduces new factors such as objectivity, reliability, and diversity of content. Consequently, the system promotes media variety, counters one-sided consumption, and ensures maximum transparency.
RISC Software GmbH is happy to support you in the submission and implementation of (research) projects in the field of Natural Language Processing.
This project was funded by the Austrian Research Promotion Agency (FFG).
Data Scientist
