Game Recommendations - How It Works

The AI-driven recommendation system personalises game suggestions by analysing player behaviour, preferences, and game characteristics. It uses collaborative filtering and game similarity models to predict and suggest games that match players' favour

Key Considerations

• 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.

AI Models Used

1. 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.
2. Collaborative Filtering
   • Utilises the game interactions of similar players.
   • Uses collective data to recommend games aligned with player preferences.
3. Game Similarity Analysis
   • Groups games based on common characteristics: such as RTP, volatility, and paylines.
   • Suggests games similar to players' favourite or frequently played games.
4. Game Sequencing
   • Predicts future choices based on a player's sequence of played games.
   • Adjusts for lifecycle phases (e.g., new player conversion).

Recommendation Process

1. Curated Game Lists: Typically suggesting up to 20 games per player, ranked by preference.
2. Customisable Recommendations Backfill: If fewer than 20 recommendations exist, the system backfills games that align with 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.

Updating Recommendations

• 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.

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