Memory Systems in Platform Services
Interconnected Memory Across the MNKY VERSE
Core Memory Components
User Profile Memory
Persistent storage of user preferences, behavior patterns, and historical interactions that inform personalization across touchpoints.
- Preference vectors
- Behavioral patterns
- Interaction history
- Explicit feedback
Conversational Memory
Short and medium-term memory systems that maintain context during and between user conversations with agents.
- Short-term context
- Conversation history
- Multi-session continuity
- Intent tracking
Knowledge Memory
Structured repositories of domain knowledge, content, and information used by agents to provide accurate and relevant responses.
- Product information
- Educational content
- Community resources
- Procedural knowledge
Collective Memory
Aggregated insights and patterns derived from broader user population that inform general improvements without compromising individual privacy.
- Trend identification
- Common questions
- Usage patterns
- Effectiveness metrics
Platform Integration Points
Web Platform
How memory systems enhance web experiences
- Personalized content delivery
- Contextual navigation suggestions
- Preference-based product recommendations
- Continuous session handling
Dojo Platform
Memory integration in learning environments
- Progress tracking across sessions
- Adaptive learning path creation
- Knowledge gap identification
- Learning style adaptation
E-commerce
Memory-powered shopping experiences
- Purchase history integration
- Preference-based filtering
- Cart persistence
- Product affinity analysis
Community
Memory systems for community features
- Interest matching
- Content relevance scoring
- Connection recommendations
- Engagement pattern recognition
Memory Architecture
- Storage Layer
- Processing Layer
- Access Layer
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Optimized for Different Memory Types
The storage layer utilizes different technologies optimized for specific memory requirements:
- Vector Database: For semantic search and similarity matching
- Graph Database: For relationship mapping and network analysis
- Document Store: For structured knowledge and content
- Time-Series Database: For sequential and temporal data
Memory System Capabilities
Personalization Engine
Personalization Engine
How memory systems drive personalization
- Preference learning from explicit and implicit signals
- Personalization vector creation and maintenance
- Cross-platform preference synchronization
- Adaptive experience delivery based on historical context
Contextual Awareness
Contextual Awareness
Maintaining context across interactions
- Session state persistence
- Cross-session continuity
- Multi-channel context integration
- Intent and goal tracking
Learning Systems
Learning Systems
How memory systems improve over time
- Feedback incorporation mechanisms
- Performance monitoring and optimization
- Pattern identification and recognition
- Continuous model improvement
Privacy Framework
Privacy Framework
Protecting user privacy in memory systems
- Consent management integration
- Data minimization principles
- Anonymization and aggregation techniques
- Retention policies and user controls
Implementation Considerations
Implementation Guidelines
Technical Considerations
- Memory persistence strategy
- Synchronization mechanisms
- Failover and redundancy
- Performance optimization
User Experience Considerations
- Transparency about memory usage
- User control mechanisms
- Feedback loops for correction
- Progressive personalization
Privacy Considerations
- Consent management
- Data minimization
- Retention policies
- Right to be forgotten
Integration Considerations
- API design for memory access
- Event-driven architecture
- Cross-service consistency
- Versioning strategy