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Documentation Index

Fetch the complete documentation index at: https://docs.neuronsearchlab.com/llms.txt

Use this file to discover all available pages before exploring further.

User segments let you define reusable groups of users based on their behaviour, profile data, or interactions with specific items. Segments are evaluated at recommendation time and can be referenced as conditions in ranking rules, giving you fine-grained control over who sees what.

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Creating a segment

1

Open User Segments

Navigate to Console > User Segments.
2

Start a new segment

Click New Segment.
3

Name the segment

Enter a name and optional description.
4

Add conditions

Add one or more conditions. See the condition types below.
5

Activate evaluation

Toggle Active on so the segment is evaluated at request time.
6

Create the segment

Click Create Segment.
A user belongs to a segment only when all conditions match (AND logic). If you need OR logic, create separate segments and reference them independently in rules.

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Condition types

Each condition has a type, a field, an operator, and optionally a value.

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Behavioral

Counts the number of events of a given name for the user.
FieldMeaning
Event name (e.g. click, purchase)The event type to count
Example: click greater_than 10 matches users with more than 10 click events.

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Demographic

Looks up a value from the user’s metadata.
FieldMeaning
Metadata key (e.g. country, device)The user metadata field to check
Example: country equals US matches users whose metadata has country: "US".

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Computed

Checks derived statistics calculated from the user’s event history.
FieldMeaning
total_eventsTotal number of events for the user
days_since_firstDays since the user’s first recorded event
Example: total_events greater_than 50 matches users with high engagement.

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Item interaction

Checks whether a user has interacted with a specific item and how many times.
FieldMeaning
Item ID (e.g. movie_123)The specific item to check interactions for
The operator dropdown shows contextual labels:
OperatorLabelMeaning
existshas interactedUser has at least one event involving this item
greater_thaninteractions >Interaction count exceeds the threshold
less_thaninteractions <Interaction count is below the threshold
equalsinteractions =Interaction count matches exactly
not_equalsinteractions !=Interaction count does not match
Example: movie_123 exists matches users who have watched, clicked, or otherwise interacted with movie_123.

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How segments are evaluated

At recommendation time the engine:
1

Pre-fetch user data

Pre-fetches the user’s event counts, metadata, computed stats, and item interaction counts in efficient batch queries.
2

Evaluate active segments

Evaluates each active segment’s conditions against this data.
3

Return matching segments

Returns the list of matching segment IDs to the rules engine.
Segment evaluation is non-blocking. If the segment tables are unavailable, recommendations still return without segment data.

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Using segments in rules

Once a segment exists, you can target a rule to it:
1

Open the rule editor

Open the rule editor. See Rules Engine.
2

Add a segment condition

Add a condition and set the field to segment_id.
3

Use the segment dropdown

The UI shows a dropdown of all available segments instead of a free-text field.
4

Select the target segment

Select the segment you want to target.
The rule then only applies to users who belong to that segment at request time.

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Tips

  • Start broad, then narrow. Create a simple segment first, verify it works via the Explainability page, then refine conditions.
  • Combine with scheduling. A segment like “users who haven’t watched Series X” paired with a scheduled rule creates a time-bounded promotional campaign.
  • Item interaction is powerful. Use it to distinguish between users who have started content vs. those who have never seen it — critical for upsell and completion campaigns.