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.
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Significant Activity & Favourite Games
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Detects significant player sessions.
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Filters out low-engagement or promotional sessions.
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Defines "favourite games" based on player activity, recency and other behavioural factors.
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Collaborative Filtering
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Utilises the game interactions of similar players.
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Uses collective data to recommend games aligned with player preferences.
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Game Similarity
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Groups games based on common characteristics: such as RTP, volatility, theme, game features and paylines.
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Suggests games similar to players' favourite or frequently played games.
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Game Sequencing
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Predicts future choices based on a player's sequence of played games.
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Adjusts for lifecycle phases (e.g. new player conversions).
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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.
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Curated Game List: By design the system typically suggests up to 20 games per player, ranked by preference, although this is configurable.
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Customisable Backfill: If fewer than 20 recommendations exist, the system backfills games that align with the player base preferences.
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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.
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Frequency: Recommendations can be updated in real-time or batch, with the most common update frequency being daily.
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Player rescoring: Occurs based on significant new player activity or after 7 days of inactivity.
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Maintenance with lack of activity: Recommendations may remain similar if no significant new activity is detected.
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Last updated date: Each recommendation batch includes the most recent rescored date.
For more information:
- Learn how we measure performance to showcase ROI and other key metrics
- Refer to our integration requirements guide for Amplifier AI Game Recommendations integration
Still have questions? Check out our FAQs for further assistance.