Game Recommendations - How It Works

Our AI recommendation engine personalises game recommendations by analysing player behaviour, preferences and game attributes, using a set of machine learning models to deliver relevant results

How Our AI Models Drive Game Recommendations

To deliver accurate and relevant game recommendations, the engine leverages a set of AI models that analyse signals from player behaviours, player preferences, session patterns and game attributes. Each model contributes a different perspective on what makes a game likely to resonate with a player.

• Significant Activity & Favourite Games

  • Detects significant player sessions.

  • Filters out low-engagement or promotional sessions.

  • Defines "favourite games" based on player activity, recency and other behavioural factors.

• Collaborative Filtering

  • Utilises the game interactions of similar players.

  • Uses collective data to recommend games aligned with player preferences.

• Game Similarity

  • Groups games based on common characteristics: such as RTP, volatility, theme, game features and paylines.

  • Suggests games similar to players' favourite or frequently played games.

• Game Sequencing

  • Predicts future choices based on a player's sequence of played games.

  • Adjusts for lifecycle phases (e.g. new player conversions).

Key Considerations

These are key factors the system considers when generating personalised recommendations

• Stake Amounts: The amount wagered by players in their sessions.
• Session Frequency: Frequency of game sessions.
• Preferred Games: Regularly chosen games.
• Frequently Played Titles: Top game titles revisited by player.

Recommendation Process

The recommendation process balances personalised curation with fallback options to ensure a full list of relevant games.

1. Curated Game List: By design the system typically suggests up to 20 games per player, ranked by preference, although this is configurable.

2. Customisable Backfill: If fewer than 20 recommendations exist, the system backfills games that align with the player base preferences.

3. Model Outputs: The recommendations can be delivered as independent feeds, e.g. via individual API requests, or blended sets, e.g. singular API request.

Updates & Frequency

Recommendations are regularly updated based on player activity and system schedules to keep suggestions fresh and relevant.

• Frequency: Recommendations can be updated in real-time or batch, with the most common update frequency being daily.

• Player rescoring: Occurs based on significant new player activity or after 7 days of inactivity.

• Maintenance with lack of activity: Recommendations may remain similar if no significant new activity is detected.

• Last updated date: Each recommendation batch includes the most recent rescored date.

Still have questions? Check out our FAQs for further assistance.