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