Offline Scoring

Offline scoring validates a run and produces recommendations in bulk, before any real-time deployment. Both paths use the same math: dot product of L2-normalized deepfeatures embeddings.

Concept test (fast path)

A concept test ranks a fixed list of offers for a single customer. It is the quickest way to confirm a run’s data and metadata are sound.

Request (POST /api/v1/algorithms/two-tower/concept-test):

{
  "run_id": "tt_abc123",
  "customer_id": "user_1",
  "offers": ["ProductA", "ProductB", "ProductC"]
}

Response:

{
  "success": true,
  "run_id": "tt_abc123",
  "customer_id": "user_1",
  "ranked": [
    { "offer": "ProductB", "score": 0.87 },
    { "offer": "ProductC", "score": 0.41 },
    { "offer": "ProductA", "score": 0.12 }
  ],
  "detail": "H2O DL tower scoring"
}

Batch scoring

Batch scoring writes top-K recommendations per customer into a MongoDB collection. It iterates distinct customers and offers for the run’s predictor and applies the concept-test ranking to each customer.

Request (POST /api/v1/algorithms/two-tower/batch-score, async job):

{
  "run_id": "tt_abc123",
  "top_k": 10,
  "max_users": 5000,
  "scores_collection": "two_tower_scores"
}

The job id is returned; poll GET /api/v1/jobs/{job_id} for progress. Each output document:

{
  "run_id": "tt_abc123",
  "customer_id": "user_1",
  "ranked": [
    { "offer": "ProductB", "score": 0.87 },
    { "offer": "ProductC", "score": 0.41 }
  ],
  "created_at": "2026-06-30T00:00:00Z"
}

From offline to online

Offline scoring proves the embeddings are meaningful. To serve recommendations per request — with logging, audit, and the campaign contract — move to Real-Time Scoring, which ranks the offer matrix using precomputed (or live PyTorch) embeddings.