User Profiling and Personalization
The user profiling system enables our agents to adapt their interactions, recommendations, and content to each individual’s preferences, needs, and behaviors. This personalization creates more relevant, engaging, and effective experiences across the MOOD MNKY ecosystem.Profile Components
A user’s profile consists of multiple layers of information that together create a comprehensive understanding of their preferences and needs:Explicit Preferences
Directly stated preferences provided by the user through settings, questionnaires, and direct feedback
Implicit Preferences
Observed behavioral patterns derived from interactions, content engagement, and product usage
Interaction Patterns
Communication style, response preferences, and engagement frequency
Product Affinities
Preferred product categories, scent preferences, and usage patterns
Learning Attributes
Learning style, pace, and topics of interest
Community Engagement
Participation patterns, connection preferences, and contribution styles
Profile Development Process
The user profile evolves over time through a continuous process of data collection, analysis, and refinement:1
Initial Setup
Basic profile creation through onboarding questionnaires and preference settings.
2
Progressive Enhancement
Ongoing enrichment through interactions, behavior tracking, and explicit feedback.
3
Regular Review
Periodic confirmation of preferences and suggestions for profile updates.
4
Correction Mechanisms
User-driven adjustments to correct misaligned personalization.
5
Multi-dimensional Expansion
Development of new personalization vectors as the relationship deepens.
Personalization Vectors
Our system uses several personalization vectors to adapt the user experience:
Content Personalization
- Recommendation algorithms tailored to content type and user preferences
- Difficulty level adaptation based on skill level and learning history
- Format preference consideration (text, video, interactive, etc.)
- Topic interest weighting to prioritize relevant content
- Contextual relevance assessment based on current goals and needs
Interaction Style Personalization
- Communication style matching to align with user preferences
- Detail level adjustment based on comprehension and interest
- Technical language calibration to match expertise level
- Rhythm and pace adaptation for conversation flow
- Tone and humor adjustment to match personality preferences
Product Recommendations
- Scent profile matching based on preferences and history
- Product category affinities derived from browsing and purchase history
- Purchase pattern analysis for timing and frequency
- Occasion-based suggestions tied to calendar and life events
- Complementary product identification for complete experiences
Data Sources and Integration
Platform Interactions
Platform Interactions
User activities across web, mobile, and other digital touchpoints, including page views, feature usage, and time spent.
E-commerce Data
E-commerce Data
Purchase history, browsing behavior, wish lists, and product interactions from our Shopify integration.
Agent Conversations
Agent Conversations
Interaction history with agents, including topics discussed, questions asked, and feedback provided.
Dojo Platform Activity
Dojo Platform Activity
Learning progress, content engagement, and community participation within the Dojo platform.
Explicit Input
Explicit Input
Preferences directly stated through questionnaires, settings, and direct feedback.
Adaptation Mechanisms
Our systems adapt to user preferences through several mechanisms:Dynamic Adaptations
- Dashboard organization that prioritizes frequently used features
- Content recommendation algorithms that learn from engagement
- Interface adaptation based on usage patterns
- Notification customization aligned with communication preferences
- Agent interaction style tailored to individual communication preference
Privacy and Control
Our personalization system is designed with privacy and user control as core principles:- Transparent data collection with clear explanations of how information is used
- Granular permission settings for different types of data collection
- User-controlled profile editing to correct or remove information
- Privacy-preserving analytics that minimize personal identifiers
- Regular data reviews with options to purge historical data
Implementation Technologies
The personalization system leverages several technologies:- Supabase for secure user profile storage
- Vector embeddings for representing user preferences
- Machine learning models for preference prediction
- Real-time analytics for behavior tracking
- Recommendation engines for personalized content and products
Integration with Agents
Agents access and contribute to user profiles through standardized APIs:- Profile retrieval before and during interactions
- Preference discovery through conversational techniques
- Profile updating based on new insights and explicit preferences
- Adaptation mechanisms for personality, tone, and content
- Feedback collection to improve personalization accuracy
Future Developments
Planned enhancements to the personalization system include:- Emotional state adaptation to respond to user’s current mood
- Cross-device synchronization of preferences and behaviors
- Predictive preferences that anticipate needs before explicitly stated
- Time-aware personalization that adapts to different times of day or seasons
- Group personalization for shared experiences with friends or family
While personalization significantly enhances the user experience, all agents are designed to provide helpful, accurate assistance even with minimal profile information. The system continuously balances personalization with privacy and makes no assumptions that could negatively impact the experience.