Skip to main content
Use this guide to ship personalized recommendations in production surfaces such as home feed, product detail rails, and post-purchase modules.

​
Setup

1

Create an API client

Create an API client in Console > SDK Credentials.
2

Exchange credentials

Exchange client credentials for an access token.
3

Serve from the backend

Store the token in your backend service and request recommendations from server-side code through the SDKs or Core API.
curl -X POST https://auth.neuronsearchlab.com/oauth2/token \
  -H "Authorization: Basic <base64(client_id:client_secret)>" \
  -H "Content-Type: application/x-www-form-urlencoded" \
  -d "grant_type=client_credentials"

​
Common workflows

​
Request recommendations for a known user

curl -X GET "https://api.neuronsearchlab.com/v1/recommendations?user_id=user-123&context_id=101&limit=8" \
  -H "Authorization: Bearer <access_token>"

​
Serve recommendations for anonymous traffic

Use a temporary user identifier (for example a session-scoped UUID) in user_id so traffic can still be tracked consistently.

​
Roll out by surface using context IDs

Create separate context IDs for each surface (101, 205, 309) and pass the right context_id per request so ranking logic stays predictable.

​
Example app journey: Home feed rollout

1

Define the home feed context

Product team defines context 101 for Home Feed in Console.
2

Request recommendations

Backend requests recommendations with user_id, context_id=101, and limit=8.
3

Render the rail

Frontend renders the top recommendations in a hero rail.
4

Submit feedback events

Click/view events are submitted to /v1/events to close the feedback loop.
5

Review engagement

Analytics is reviewed daily for serve volume and engagement lift.

​
Next steps