algo-ecom-rankinglisted

# 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