Overview

Amazon has deployed CoSMo (Common Sense Model), an AI-driven search framework that replaces the legacy A9 algorithm's keyword-matching approach with intent-based product discovery. The system uses a knowledge graph containing over six million nodes and twenty-nine million relationship edges to interpret what customers actually want rather than simply matching the words they type. For sellers, this represents a fundamental shift in how listing optimization works.

What's Changing

• Knowledge graph over keyword matching — CoSMo connects products, attributes, and customer intents through millions of relationships, surfacing products based on contextual fit rather than exact keyword placement.
• Custom language model — A purpose-built model called CoSMo-LM analyzes behavioral patterns and generates commonsense explanations for customer actions, validated through tens of thousands of human annotations.
• Dynamic navigation — Search result pages now display context-aware refinement options based on inferred intent rather than static category filters.
• Multi-signal ranking — CoSMo balances relevance, context alignment, quality signals, behavioral patterns, and commonsense appropriateness instead of weighting a single metric like sales velocity.
• Rufus integration — CoSMo powers Amazon's conversational AI shopping assistant Rufus, enabling natural language product recommendations.
• Behavioral learning — The system learns from co-purchase patterns, search-then-buy sequences, and browsing paths to infer product relationships beyond keyword associations.

How CoSMo Differs From A9

The A9 algorithm ranked products based on mechanical factors: keyword relevance, click-through rate, conversion rate, and sales velocity. Sellers could follow a predictable playbook of identifying high-volume keywords, placing them strategically in listings, driving external traffic, and watching rankings climb.

CoSMo operates differently. When a customer searches for "winter clothes," the old system found products containing those exact words. CoSMo infers the underlying need for warmth and can surface cold-weather products even if a listing never uses the word "winter." The algorithm cares less about keyword density and more about whether a product demonstrably solves the customer's problem. The strategic question shifts from "what keywords should I rank for?" to "what problem does my product solve?"