Content Recommendations Across VOD, Magazines and HbbTV in European Media

FTV Prima faced increasing pressure to deliver personalised video content to millions of users across its various platforms, including VOD and HbbTV. The previous system, which relied on manual content curation by editorial staff, could not keep pace with the volume of available content or the growing user base. This inefficiency limited the ability to provide seamless viewing experiences and hindered broader business goals such as increasing watch time, improving retention, and boosting advertising revenue. Addressing this issue became critical to maintaining user engagement and ensuring long-term competitiveness.

Recombee delivered real-time, AI-powered recommendations across FTV Prima’s digital ecosystem, including the prima+ VOD platform, and linear TV via HbbTV. A unified database connected user identities across platforms, enabling consistent, personalized suggestions on web and mobile apps. Sophisticated algorithms powered over 55 unique recommendation scenarios, each tailored to specific user contexts. The fully automated system improved discovery and engagement by serving relevant content at scale, regardless of platform or device.

Editorial teams, business team, and end customers (who receive the recommendations)

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-Collaborative Filtering Recommendation Models (Deep Variational Autoencoders, Matrix Factorization, Linear Models, …) -Content-Based Recommendation Models (Convolutional Neural Networks, LLMs, visual similarity) -Reinforcement Learning Recommendation Models (Multi-Armed Bandit Models, Deep Reinforcement Learning) -beeFormer Transformer Architecture

Executive leadership (C-level)

Joint implementation with the client

Recombee’s internal team maintains the service provided.

Not applicable, as we are a system as service.

There are no technical requirements, the payment for SaaS is on a monthly basis.

Following the launch of the prima+ VOD platform, personalized recommendations helped users discover more relevant titles, encouraging longer watch sessions and increased interaction. This improvement extended to linear TV via HbbTV, where recommendations became more targeted and actionable. A consistent user experience across devices helped boost engagement and shifted more users into high-activity categories.

Video views rose by 34% on prima+, while ad views jumped by 73%. The number of heavy users (those watching more than three titles per month)more than doubled (2.5x increase). On HbbTV, click-through rates increased by 2.3x, highlighting improved relevance and user intent across the platform.

“Recombee allows us to modify how we want the recommendations to behave across our platforms in very specific use cases. The implementation of Recombee on the VOD platform prima+ helped us to increase video views by 34% and ad views by 73%, resulting in a significant rise in advertising revenue. Another major success has been the growth of recirculation, which is our top priority on online magazine platforms. We are currently discussing extending their recommendations also to our emails. Highly valued partnership!” – Jan Lajka, Chief Data Officer at FTV Prima

Some key factors for success: -Client’s technical readiness: The integration of recommendations itself is not technically complex; however, it is necessary to have the basic catalog data in good order and to identify users. -Flexibility and customization options of Recombee: Clients appreciated that they could fine-tune the behavior of recommendations according to specific use cases. -Teamwork and support from Recombee: Fast and responsive support, active involvement in finding solutions for unique requirements, and a professional approach helped achieve the desired results quickly.

The biggest challenge is ensuring data consistency and measurement. It is important to guarantee that the same data is available both on the client’s side and in Recombee in order to correctly measure and optimize KPIs. The Recombee team, in cooperation with the client, ensures this (through checks, report exchanges, etc.) before the start of an A/B test.

Some of our recommendations: -Choose the right metric you want to optimize: Define your main KPI – e.g., click-through rate, conversion rate, time spent, etc. – that you want to maximize. -Select an appropriate first use case: Suitable are typically placements that are seen by many users (allowing models to be optimized quickly and significant A/B test results to be obtained fast) and are not complex to implement. Most often, these are placements on the homepage or on article/video/product detail pages. -Invest in data preparation: Identify users and maintain a consistent catalog. -Leverage the system’s flexibility: Don’t be afraid to design specific personalization use cases with the help of a combination of Logics, filters, boosters, and other tools.