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Outperforming and Replacing In-House Recommender System of News Articles

Headquartered in Prague, Mafra is the largest media group on the Czech market delivering the latest local and foreign news. Its online activities reach up to 8 million monthly users, with up to 45 million recommendation requests per month.

Recombee’s smart content recommendations helped Mafra personalize news to each of their readers across three online media outlets (iDnes, Lidovky, Expres) and emailing to their premium users.

Implementation of Recombee’s sophisticated system has led to a significant increase in both click-through rate and readers’ satisfaction.

Download Case Study (PDF)
40%
Higher CTR of suggested articles
25%
Improvement in emailing CTOR

Situation

  • Desire to find the right content personalization solution (replacing in-house solution)
  • Potential to deploy across various media brands
  • Huge traffic consisting of millions of monthly readers

Requirements

  • Targeting specific content to the people who will find it most relevant
  • Retaining consumers’ trust by providing unbiased information and recommendations
  • Email personalization for iDNES Premium users

Solution

  • Above 65 distinctive scenarios on different websites and various platforms
  • Enriching the cultural life of consumers by encouraging the discovery of new content and information
  • Real-time collaborative filtering, text processing and reinforcement learning
  • Overcoming small amount of fully sorted articles to still provide highly personalized emails

Benefits & Results

  • Highly outperformed internal read-next recommender system
  • 40% increase in click-through rate of suggested articles
  • 25% improvement in emailing click-to-open rate compared to the previous solution
  • Time saved by automatically organized content and increase in user engagement

Scenarios

Read Next

Items to User scenario used across three different media outlets - idnes.cz, lidovky.cz and expres.cz.

Personalized recommendations at the bottom of each article to maximize user engagement and important KPIs like ATS (average time spent) and PVs (page views) per session.

Thanks to a sophisticated combination of real-time collaborative filtering, text-processing, and reinforcement learning models, Recombee is able to immediately respond to newly published articles, and even automatically identify breaking news, making sure that users are always offered highly relevant content that they shouldn't miss.

Emailing for Premium Users

The scenario used in the emailing campaigns recommending fresh daily premium content for subscribed users.

Significant increase of the CTOR, despite a relatively small set of articles being sorted for each user every day (there are about 20 daily Premium articles).

The solution benefits from the live recommendations deployed on-site, where a lot of feedback is collected about user engagement with the freshly published articles just before the campaign is executed. Thanks to Recombee real-time collaborative filtering and reinforcement learning models, even a few of the exposure to the newly published articles bring enough data for an excellent performance.

“We conducted A/B testings of multiple recommendation engines to find the best content personalization solution. Out of all solutions, only Recombee outperformed our internal read-next recommendations of news articles. After long-lasting A/B testing, Recombee achieved 40% higher CTR of suggested articles, which ultimately led to deployment of the solution to most of our news sites (iDNES, Lidovky, Expres).“

Petr Kelin
Manager at MAFRA, a.s.
Mafra

About Mafra

The MAFRA Media Group reaches its diverse pool of readers and site visitors through print and internet media. Offering exclusive content and quality entertainment with fresh news for the local demography, Mafra provides clarity and easy orientation of the latest events.

Their portfolio covers products from every spectrum of the media market, including newspapers, magazines, video portals, tv stations, virtual operators, and radios.