Personalized Game Difficulty Prediction Using Factorization Machines

Title of the Paper

Personalized Game Difficulty Prediction Using Factorization Machines

Paper Information

• Domain: Human-Computer Interaction and Game Design, specifically game difficulty prediction and player modeling
• Keywords: Factorization Machines, game difficulty prediction, player modeling, difficulty adaptation, machine learning, recommender systems, data-driven, user experience, difficulty adjustment, personalized models

Research Background and Problem

• Problem or Challenge:

  • Task difficulty in games is a critical factor influencing user experience, but precise difficulty prediction is challenging due to player diversity.
  • Current difficulty estimation methods based on overall player groups fail to account for individual skill differences, potentially overlooking atypical players and resulting in poor generalization.
  • Game designers often use Dynamic Difficulty Adjustment (DDA) to modify game parameters to suit players, but fully automated difficulty adjustment methods can be overly complex, and designers prefer to retain some control over the gaming experience.

• Significance:

  • Accurate task difficulty prediction is essential for optimizing user experience, particularly in video game design. Appropriate game difficulty can enhance players' learning experience, increase challenges, and improve enjoyment.

• Research Motivation and Related Work:

  • Traditional difficulty prediction methods focus on overall player group difficulty prediction, lacking individual player behavior modeling.
  • Certain technical methods (e.g., Random Forest, Bayesian Optimization) provide some personalized modeling but face scalability limitations in large-scale commercial games.
  • Factorization Machines (FM) from the recommender systems domain exhibit strong capabilities in personalized data modeling, but their potential in player behavior prediction remains underexplored.

Solution

• Proposed Method:

  • A novel personalized game difficulty prediction framework based on Factorization Machines (FM) is proposed, modeling interactions between players and game levels to estimate the number of attempts players make to complete a level.
  • The method integrates traditional techniques (e.g., Random Forest) with FM's innovative features and is compared through experiments on a large-scale game dataset.

• Innovations:

  • Applying Factorization Machines from the recommender systems domain to personalized game difficulty prediction, integrating high-dimensional interaction data.
  • Using FM to learn latent variable models, providing insights into how player and level characteristics influence game difficulty.
  • While achieving personalized difficulty prediction, the method also offers data interpretability, supporting game designers in optimizing the design process.

• Implementation Steps and Key Techniques:

  1. Formulating the prediction task: Modeling game difficulty prediction as a regression problem by estimating the number of attempts players make to complete specific levels.
  2. Comparing three methods in experiments: Non-personalized baseline (Naive Baseline), Random Forest model (RF), and Factorization Machines (FM and its extensions).
  3. Evaluating model performance: Using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) to assess prediction accuracy.
  4. Interpreting FM model parameters (e.g., bias terms and latent variables) to explore player-level behavioral characteristics.

Research Results

• Specific Findings:

  • The FM model significantly outperforms the non-personalized baseline and Random Forest methods, especially with more observation data (>20 player-level observations).
  • The first latent variable (v1) in FM is interpreted as a measure of player skill and level randomness.
  • The model demonstrates that while multi-factor models may improve performance for high-difficulty levels, they may also lead to overfitting.

• Advantages and Contributions:

  • Compared to the non-personalized baseline, the FM model achieves significantly reduced prediction errors in RMSE and MAE.
  • The FM model is task-independent and enables interpretive analysis of data, providing game designers with insights into player grouping and level maintenance.
  • The method is highly scalable and capable of handling large datasets.

• Experimental or Evaluation Results:

  • Experiments were conducted using a dataset containing approximately 700,000 players and over 6,000 levels. Across multiple observation levels (10, 20, 30, 50, 100 levels), FM consistently outperformed existing methods.
  • In predicting the next 150 levels, FM's MAE was close to that of the Random Forest method but remained stable under conditions of scale and data sparsity.

• Limitations and Future Directions:

  • Cold-start problem: The model cannot immediately provide high-quality predictions for new players and levels.
  • The richness of data features significantly impacts model performance, necessitating further optimization of feature engineering in future work.
  • Lack of explicit modeling of dynamic player behaviors (e.g., learning curves).

Conclusion

This study introduces a novel and effective solution for personalized game difficulty prediction based on FM, demonstrating its potential application in large-scale commercial game environments. Future work could extend this method to other game types or challenge domains and further explore dynamic modeling of player behavior changes.