AI at Play: Revolutionizing iLottery Engagement

Brightstar’s new Game Recommendation Engine provides players with an enhanced, personalized iLottery experience.

By Brightstar Lottery

Published January 20, 2026

Today’s iLottery games offer challenges to their creators and players. One challenge is that players can experience choice overload. With some lotteries offering more than 100 active eInstant games in their catalogue, players can feel overwhelmed. Without guidance, they may struggle to discover eInstant games that match their preferences or playing style. Players who do not find relevant or exciting games may disengage. This leads to lower session times and fewer repeat visits due to low engagement and retention. Without personalized recommendations, lotteries may fail to surface eInstant games that are trending or have high conversion potential, resulting in missed revenue opportunities.

That is why Brightstar Lottery has introduced the Game Recommendation Engine. Like Netflix, Brightstar’s Game Recommendation Engine provides an enhanced, personalized experience.

The Game Recommendation Engine algorithm uses player behavior and game attributes to uncover preferences, providing tailored suggestions such as: “Because you played X, you might like Y,” “Trending games among similar players," and “Games similar to your favorites.” The Game Recommendation Engine introduces players to new and relevant eInstant games, increasing satisfaction and excitement. 

Delivering personalized suggestions in the game discovery process highlights the games that the individual player finds most interesting, which reduces the need to search through options they may find less appealing. This makes the experience smoother and more enjoyable.  

Brightstar’s Game Recommendation Engine provides lotteries with an opportunity for data-driven optimization, with insights informing game design, marketing strategies, and game portfolio management. Its algorithm analyzes player-game interaction data, such as game play, wagering, and session. By decomposing this data, latent features can be identified, providing hidden characteristics of both player behaviors and games.

“Typically, players engage with only a handful of favorite games they rotate,” says Karri Paavilainen, Vice President, Services, Brightstar Lottery (pictured above). “Operators want players to explore more content to build frequency and retention. That’s why portfolios expand, but discovery remains a challenge. Many operators now offer 100 to 300 games, and players wonder: Which ones are for me? I’m not going to try them all. This is where personalization shines. Think Netflix: instead of endless scrolling, you get a curated list based on your habits. Our Game Recommendation Engine does the same for eInstants, making finding games effortless and engagement deeper. Major online services such as Facebook, Instagram, Amazon, and Netflix all use algorithms to personalize content. Lottery should be no different.”

Brightstar’s Game Recommendation Engine uses a hybrid approach that combines the strengths of player interaction data and content data. Instead of treating users and items as isolated entities, they are represented as a sum of the latent representations of their features. This allows the model to learn more general patterns from the data. Using principal components helps reduce the total number of parameters the model needs to learn.  

Market Impact 

Brightstar’s Game Recommendation Engine has had a meaningful impact since its 2025 launch. Following implementation at a North American lottery, a significant uptick in user engagement has been observed, with a growth of 35% in wager transactions. 

One of the goals for the Game Recommendation Engine is to encourage players to discover new eInstant games via cross-selling. Since launch, the number of different games played per user has increased by 24%.  

An A/B test for Game Recommendation Engine performance measurement was done with a control and test group to measure differences between the two groups. The experiment involved splitting players into two groups; the Test Group received personalized recommendations from the recommendation engine while the Control Group did not receive any personalized recommendations.  

During the test period, players who were given personalized recommendations engaged with 62% more eInstant game titles, on average, compared to those in the Control Group. The Game Recommendation Engine was able to improve discovery of new games for these players. Additionally, players who were given personalized recommendations made more wagers compared to those in the Control Group. “That’s not just a better experience; it’s measurable impact,” says Paavilainen. “Personalization drives engagement, expands portfolio utilization, and boosts revenue. For operators, it’s a win-win: happier players and stronger performance.”

The Game Recommendation Engine technology has been shared with other lotteries that are keen to adopt it.

Lotteries want to incorporate this capability into their iLottery eInstant game launch plan so that recommendations can be used to promote new games and track engagement over time. The immediate plan is to expand the use of the Game Recommendation Engine across Brightstar’s customer base. Expansion paves the way for additional API-based services to be integrated with front-end products (portal and mobile apps).  

With these new APIs, lotteries will be able to offer personalized recommendations by having additional carousels recommending games by categories, such as Themes, Mechanic, and “Other players who played this also played….”  Inspired by the successful use of AI by brands such as Amazon and Netflix, the Brightstar team is working on continuous enhancements to the Game Recommendation Engine algorithm by engineering more features to capture player and game characteristics.

“Our aim is to support Brightstar customers in offering their players an experience that is like the ones they have with other top brands they engage with for digital content,” says Paavilainen. “Lotteries can leverage the Game Recommendation Engine to grow their player base, stay competitive in a growing digital landscape, and generate more revenue for good causes.” 

Brightstar is expanding AI-driven player experience improvements across the entire lifecycle. “Expect more personalized marketing campaigns, smarter promotions, and elevated customer service experiences, all designed to meet the expectations of today’s digital-savvy players,” says Paavilainen. “We’re also exploring predictive analytics for responsible gaming and dynamic content personalization for seasonal events. As consumer behavior evolves, we’ll keep innovating to deliver experiences that feel fresh, relevant, and rewarding. The future of lottery play isn’t just about more games; it’s about smarter, more personalized engagement.”

To learn more about the Game Recommendation Engine, contact your Brightstar representative.