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Contexts describe the placement, audience, and intent behind a recommendation request. Properly configuring them keeps results relevant across your product surfaces.

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When to create a new context

Create a dedicated context whenever:
  • The UI surface has unique layout or business goals (homepage vs. detail page).
  • You need a different blend of filters, boosts, or fallback logic.
  • You want to measure performance separately from other experiences.
Start with a handful of high-impact contexts, then expand as you identify more nuanced placements.

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Configure contexts in the console

  1. Navigate to Console → Contexts.
  2. Click New context and enter a numeric Context ID (for example 101). The ID is stored as a number and is the value your engineering team will send in API calls.
  3. Fill in the modal to define how the context should behave:
    • Context Name and Description document the placement.
    • Select an Associated Model and Recommendation Type once the underlying model exists.
    • Configure Filters and optional combinations (AND/OR) to enforce inventory rules.
    • Add Group By Fields to aggregate items by shared metadata.
    • Configure one or more Boosters (e.g., popularity +25%) to highlight seasonal campaigns.
    • Specify Influence Rules when certain metadata should dominate the ranking.
    • Toggle Additional Options such as including random content in recommendations.
    • Use Preview Recommendations to validate the setup for a specific user or a random user before saving.
  4. Save the configuration and share the numeric context ID with your engineering team.

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Reference contexts in API calls

Attach the context ID to every recommendation request so NeuronSearchLab can apply the proper logic. 
curl -X GET "https://api.neuronsearchlab.com/recommendations" \
  -H "Authorization: Bearer <access_token>" \
  -G \
  --data-urlencode "user_id=user-123" \
  --data-urlencode "context_id=101"
The context you choose also appears in analytics, allowing you to compare conversion rates and engagement across placements.

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Iterate continuously

Monitor performance metrics and adjust context settings as business goals evolve. Combine live experimentation with event tracking to ensure the learning loop stays tight.