Gaming Recommendation System Using Player History Data
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Solution Overview
Problem
Existing systems fail to effectively recommend electronic gaming machines to players based on their individual preferences, leading to a lack of personalized promotions and inefficient use of casino resources.
Innovation Solution
A computer-implemented recommendation system that collects player history data, analyzes it to generate a matrix of similar games, and provides personalized recommendations through a player interface, leveraging both casino and player-provided data to match preferences and promote games accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system collects and analyzes detailed player history data to generate personalized recommendations, then recommendation accuracy and player engagement improve, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments player data into distinct categories (gaming preferences, spending patterns, play frequency, game types) and processes each segment separately through specialized analytics modules. This segmentation allows the complex data processing to be divided into manageable components, improving recommendation accuracy while organizing system complexity into structured, maintainable segments.
Solution Approach 2:
The system transforms raw player history data into a multi-dimensional preference matrix that captures player preferences across multiple dimensions (game types, time of day, bet sizes, win/loss patterns). By adding these dimensional layers to the data, the system achieves higher recommendation precision without merely increasing complexity linearly, as the dimensional structure provides organized frameworks for analysis.
2Reliability
If the system provides personalized game recommendations to each player, then player loyalty and engagement increase, but implementation cost and operational complexity increase
Solution Approach 1:
The system implements self-service mechanisms where players can access and review their personalized recommendations through user-friendly interfaces, and the system automatically updates recommendations based on ongoing player behavior without requiring manual intervention. This reduces operational complexity while maintaining high player loyalty through consistent, personalized service.
Solution Approach 2:
The system incorporates feedback loops where player responses to recommendations (whether they play recommended games or provide direct feedback) are continuously fed back into the analytics engine to refine future recommendations. This automated feedback mechanism strengthens player loyalty through increasingly accurate personalization while reducing the need for complex manual operational adjustments.
3Measurement precision
If the system processes comprehensive player data including time played and bet size, then recommendation quality improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing of player data by pre-calculating and storing key metrics (total time played, average bet size, preferred game categories, spending patterns) as players generate gameplay data. This preliminary action ensures that when recommendations are needed, the system can quickly retrieve and analyze pre-processed metrics rather than computing everything from raw data, maintaining high recommendation quality while reducing processing time.
Solution Approach 2:
The system dynamically adjusts processing parameters based on priorities and available resources, such as processing intensity, data granularity, and update frequency. For time-sensitive recommendations, the system can use simplified parameter sets for faster processing, while for less time-critical updates, it performs more comprehensive analysis, thus balancing recommendation quality with processing time requirements.
Data Source
AI summary
A recommendation system is provided, including a non-transitory memory, a processor, and a player interface. The non-transitory memory is configured to store a database including the player's playing history for a plurality of electronic gaming machines. The processor is coupled to the non-transitory memory and configured to gain access to the database and execute computer-executable instructions. The computer-executable instructions include a promotions engine operable to generate a list of electronic gaming machine recommendations personalized for a player based at least on the player's playing history. The promotions engine is further operable to generate a promotion based on the list. The player interface is accessible by the player and includes a display configured to present the promotion.