# User preference memory strategy
<a name="user-preference-memory-strategy"></a>

The `UserPreferenceMemoryStrategy` is designed to automatically identify and extract user preferences, choices, and styles from conversational data. This lets your agent to learn from interactions and builds a persistent, dynamic profile of each user over time.

 **Steps in the strategy** 

The user preference strategy includes the following steps:
+  **Extraction** – Identifies useful insights from short-term memory to place into long-term memory as memory records.
+  **Consolidation** – Determines whether to write useful information to a new record or an existing record.

**Note**  
The user preference strategy processes only `USER` and `ASSISTANT` role messages during extraction. For more information about roles in agent conversations, see [Conversational](https://docs.aws.amazon.com/bedrock-agentcore/latest/APIReference/API_Conversational.html).

 **Strategy output** 

The user preference strategy returns JSON objects with context, preference, and categories, making it easier to capture user choices and decision patterns.

 **Examples of insights captured by this strategy include:** 
+ A customer’s preferred shipping carrier or shopping brand.
+ A developer’s preferred coding style or programming language.
+ A user’s communication preferences, such as a formal or informal tone.

By leveraging this strategy, your agent can deliver highly personalized experiences, such as offering tailored recommendations, adapting its responses to a user’s style, and anticipating needs based on past choices. This creates a more relevant and effective conversational experience.

 **Default namespace** 

 `/strategy/{memoryStrategyId}/actors/{actorId}/` 

**Topics**
+ [System prompt for user preference memory strategy](memory-user-prompt.md)