"algo-ecom-ranking"

E-Commerce Product Ranking

Overview

E-commerce ranking combines text relevance (BM25) with commercial signals (CTR, conversion rate, revenue, margin) into a unified ranking score. Uses learning-to-rank (LTR) models trained on click and conversion data to optimize for business-relevant outcomes.

When to Use

Trigger conditions:

• Building a product search/browse ranking beyond pure text relevance
• Incorporating business metrics (margin, inventory) into ranking
• Implementing a learning-to-rank pipeline

When NOT to use:

• For pure text search relevance only (use BM25)
• When no click/conversion data exists (start with rule-based ranking)

Algorithm

IRON LAW: Relevance Is Necessary But NOT Sufficient for E-Commerce Ranking
A result that is textually relevant but has zero sales history, no
reviews, and is out of stock serves no one. E-commerce ranking must
balance: relevance (does it match the query?), quality (is it a good
product?), and commercial value (does it generate revenue?).

Phase 1: Input Validation

Collect features per product-query pair: text relevance score (BM25), historical CTR, conversion rate, average rating, review count, price competitiveness, inventory level, margin. Gate: Minimum features available, click data from 30+ days.

Phase 2: Core Algorithm

Rule-based baseline: Score = w₁×relevance + w₂×popularity + w₃×rating + w₄×recency. Manually tune weights.

LTR approach:

1. Generate training data from click logs (clicked = positive, skipped = negative, with position debiasing)
2. Features: text match, behavioral (CTR, add-to-cart rate), product quality (rating, reviews), freshness, price
3. Train: LambdaMART or gradient-boosted ranking model optimizing NDCG
4. Blend: final_score = α × LTR_score + (1-α) × business_boost

Phase 3: Verification

Evaluate offline: NDCG@10, MRR. A/B test online: revenue per search, click-through rate, conversion rate. Gate: NDCG improves over baseline, A/B test positive on primary metric.