This section explains how memory turns into personalization and continual learning — profiles, preferences, feedback, cold starts, and the ethical boundaries around remembering people.
What you will learn
- User profiles and preference learning without per-user model training
- Shared vs isolated experience across users
- Feedback-driven updates and progressive profiling
- How to evaluate personalization and where ethical limits apply
Chapters in this section
There are 16 chapters in this part. Open any chapter to read it on its own, or work through them in order.
- 1 User Profiles as a Bounded Memory Construct
- 2 Preference Learning Over Time
- 3 Feedback-Driven Memory Updates
- 4 Natural-Language Feedback as a Memory Signal
- 5 Personalized Agents Without Per-User Model Training
- 6 Shared Experience: Letting Agents Learn from All Users
- 7 Isolated Experience: Protecting Individual User Data
- 8 Conversation Summaries as Persistent Context
- 9 Building a User-Knowledge Layer
- 10 Continual Learning Without Catastrophic Forgetting
- 11 Memory-Driven Behavior Change in Agents
- 12 Evaluating Whether Personalization Is Actually Working
- 13 Cold-Start Problems in Personalized Memory Systems
- 14 Progressive Profiling: Building Understanding Incrementally
- 15 Personalization at the Team Level vs the Individual Level
- 16 Ethical Boundaries in Personalized Memory Systems