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Events are the signals your users generate when they interact with your product: views, clicks, purchases, shares, and any other action you decide is meaningful. NeuronSearchLab uses these signals to learn each user’s preferences and improve ranking quality over time. This guide explains how to define event types, set their relative importance, and organise them into templates for training.

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Event types

An event type maps a numeric ID (sent in your API or SDK calls) to a human-readable name and a weight. The weight tells the training process how much to value one signal relative to others.

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Add an event type

  1. Open Console > Events.
  2. Enter a name in the Event name field, for example click, purchase, or video_complete.
  3. Set a weight between 1 and 100. Higher weight means the training process treats that signal as more informative.
  4. Click Add event.
A common starting point:
NameWeight
impression1
click10
add_to_cart30
purchase100
You can adjust weights at any time. Changes take effect on the next training run.

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Send events from your application

Pass the numeric event ID when tracking actions via the SDK: 
await sdk.trackEvent({
  eventId: 2,         // matches the ID shown in the console
  userId: "user-123",
  itemId: "item-456",
  metadata: { placement: "homepage" },
});
Or via the REST API: 
curl -X POST https://api.neuronsearchlab.com/events \
  -H "Authorization: Bearer <access_token>" \
  -H "Content-Type: application/json" \
  -d '[{
    "event_id": 2,
    "user_id": "user-123",
    "item_id": "item-456",
    "event_ts": "2025-01-15T14:30:00Z"
  }]'

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Signal templates

A template captures a specific combination of event types, weights, and training thresholds. Saving a template lets you reproduce a training run exactly or switch between different configurations without losing your settings.

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Create a template

  1. On the Events page, configure your event types and set the thresholds described below.
  2. Enter a name in the Template name field.
  3. Set the status to Draft (not yet used for training) or Published (ready to train from).
  4. Click Save template.

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Training thresholds

Two thresholds control when a model is trained:
  • Per-signal threshold: the minimum number of events for each individual event type before that signal is included in training. Set this to avoid training on noise from rarely used signals.
  • Minimum total events: the minimum total number of events across all signals before training will proceed. This prevents a model from training on too little data to generalise.

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Train from a template

  1. Select a saved template from the template list.
  2. Configure the training options:
    • Epochs: how many passes through the training data (default 5, range 1-50).
    • Batch size: number of events processed together (64, 128, 256, or 512).
    • Learning rate: step size for gradient updates (default 0.001).
  3. Click Start training. The console will show the job status and metrics as training progresses.

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Monitoring training runs

Open Console > Training Jobs to see the status of each run, its duration, and the metrics reported at completion. From this page you can:
  • View final training metrics and run manifests.
  • Approve a trained model package for deployment.
  • Promote an approved model to the live endpoint, replacing the current production model.
  • Stop an in-progress run if needed.

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Analytics: measuring signal quality

After deploying a model, monitor how different event types are driving engagement in Console > Analytics. Track each event type as its own metric alongside recommendation serve counts to understand which signals correlate with downstream outcomes. If high-weight events are rarely occurring, consider lowering their weight or broadening what you treat as a conversion signal. If impressions are large but clicks are low, adjust your context filters and influence rules before retraining.