Recommendation system for improving user experience

The amount of text material published on web is growing rapidly, but people’s attentiveness hasn’t improved. Therefore, the critical challenge for every news site is to guide readers to products that they are interested in and to do so in a way that would help to achieve business objectives.

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STACC’s recommendation system is an analytics solution, which helps to choose from a variety of news articles the ones that might interest the reader the most. It’s an artificial intelligence system that enables to:

  • collect, store and process information about the online behavior of the readers;
  • predict user intent (what the readers wants to do in the news site);
  • predict articles that match the user profile (which articles are suitable for the reader at a particular time point);
  • set business-specific rules (which articles can’t be recommended together, which articles are subject to special rules, etc.);
  • conduct A/B testing (which recommendation model makes the best recommendations in terms of the business objective);
  • monitor the recommendation system (how readers react to the recommendation system and how accurate the system is);
  • improve the system constantly (using machine learning, the system learns to become more familiar with the reader over time and makes its recommendations more accurate).