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
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