TAREA 08: Personalization - Adaptive User Models

Status: 🔴 PLANNING

🎯 Purpose

Adaptive audio processing that learns user preferences and automatically adjusts to context (genre, mood, listening environment).

🏗️ Key Components

• User Interaction Tracking: Parameter adjustments, preset usage
• Preference Modeling: Collaborative filtering, matrix factorization
• Context-Aware Processing: Genre, time of day, mood detection
• Reinforcement Learning: Adaptive effects that improve over time

📋 Architecture

class PersonalizationEngine {
public:
    // Track user interaction
    void recordInteraction(const UserAction& action);

    // Get personalized preset recommendations
    std::vector<Preset> getRecommendations(
        const AudioContext& context,
        int num_recommendations = 5
    );

    // Adapt effect parameters to user style
    ParameterSet adaptParameters(
        const IAudioProcessor& processor,
        const AudioContext& context
    );

    // Update user model
    void updateModel(const std::vector<UserAction>& history);
};

struct AudioContext {
    std::string genre;        // "rock", "jazz", "classical"
    std::string mood;         // "energetic", "calm", "aggressive"
    std::string time_of_day;  // "morning", "evening", "night"
    std::string environment;  // "studio", "live", "home"
};

struct UserAction {
    enum Type { PresetLoad, ParameterChange, Like, Dislike };

    Type type;
    std::string effect_id;
    ParameterSet parameters;
    AudioContext context;
    float rating;  // -1.0 to 1.0
};

🎯 Use Cases

• Auto-adjust EQ based on listening history
• Smart compression that learns user style
• Genre-adaptive processing chains
• Mood-based effect recommendations
• Context-aware dynamic range

📚 Techniques

• Collaborative Filtering: User-user, item-item similarity
• Matrix Factorization: Latent factor models
• Contextual Bandits: Exploration vs exploitation
• Deep Reinforcement Learning: Policy gradient methods

🔧 Example

PersonalizationEngine engine;
engine.initialize("models/user_model.onnx");

// Track user actions
UserAction action;
action.type = UserAction::ParameterChange;
action.effect_id = "compressor";
action.parameters["threshold"] = -20.0f;
action.context.genre = "rock";
action.rating = 0.8f;

engine.recordInteraction(action);

// Get personalized recommendations
AudioContext context;
context.genre = "rock";
context.mood = "energetic";

auto recommendations = engine.getRecommendations(context, 5);

for (const auto& preset : recommendations) {
    std::cout << "Recommended: " << preset.name
              << " (confidence: " << preset.confidence << ")\n";
}

📊 Performance Targets

• Accuracy: > 75% recommendation acceptance rate
• Cold Start: Reasonable recommendations within 10 interactions
• Privacy: On-device learning, no data upload required

Priority: 🟡 Medium - Enhances long-term user experience