The Explainability page lets you trace exactly why an item appeared (or did not appear) in a user’s recommendations. It shows the raw similarity score, which rules matched, and how each pipeline stage affected the result.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.
Running an explanation
The system queries real data and returns a breakdown of the recommendation decision.
What you see
Similarity score
The raw cosine similarity between the user’s embedding and the item’s embedding, converted to a 0-1 score. This is the base relevance signal before any rules or pipeline adjustments.Applied rules
Each active rule is listed with:- Rule name and type (boost, pin, filter, etc.)
- Match status — a green matched badge if the rule’s conditions were satisfied for this user-item pair, or a grey no match badge if conditions did not match.
- Conditions — what the rule checks (item metadata fields, segment membership, etc.)
Pipeline stages
Each pipeline stage is shown with a status badge:| Status | Meaning | Colour |
|---|---|---|
| passed | Stage ran normally | Green |
| disabled | Stage is turned off in pipeline config | Grey |
| skipped | Stage was skipped for this request | Yellow |
| partial | Stage ran but with limited data | Amber |
Feature contributions
A breakdown of what signals contributed to the final score — the embedding similarity, any metadata features, and rule adjustments.Common debugging scenarios
”Why is this item ranked so low?”
Check similarity
Check the similarity score. If it is low, the user’s behaviour does not strongly align with this item’s embedding.
Check suppressing rules
Check for
bury or filter rules that matched. A bury rule reduces the score; a filter rule removes the item entirely.”Why is this item appearing when it shouldn’t?”
Look for unexpected rules
Look for
boost or pin rules that matched unexpectedly. The rule’s conditions may be broader than intended.Check segment membership
Check whether the user belongs to a segment that triggers a promotional rule.
”Why are rules not applying?”
Check the schedule
Check the schedule; the rule may have a start date in the future or an end date in the past.
Check the segment condition
Check the segment condition. The user may not belong to the target segment, so create a segment with broader conditions to verify.
Tips
- Use Explainability before launching a campaign. Test your rules against real user-item pairs to verify they behave as expected.
- Combine with Analytics. If engagement drops after deploying a new rule, use Explainability to check whether the rule is matching more broadly than intended.
- Test edge cases. Try explaining items for users who are in multiple segments, or items that match conditions for multiple rules, to understand priority interactions.