Recommendation Engines with LLM Reasoning: Beyond 'Users Like You'

Traditional recommendation systems are "Black Boxes." You get a list of 10 movies because "Other people liked them." This lacks Personal Relevance and Explainability. Graph RAG turns a recommendation into a Conversation. Instead of just showing a product, the AI can say: "I'm suggesting this laptop because you mentioned you use Premiere Pro (Graph Fact), and this laptop has the GPU that our community says is the best for that specific software (Graph Path)."

In this lesson, we will look at how to build a Reasoning-Based Recommendation system. We will learn how to link "User Affinities" to "Product Attributes" and how to use Graph RAG to generate the "Justification" for every suggestion. We will see how this "Explainability" increases user trust and click-through rates.

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1. The Recommendation Graph Schema

• (:User) {preferences, history}
• (:Product) {category, price, technical_specs}
• (:Attribute) (e.g., "Portable," "High-Power," "Budget-Friendly")
• (:Review) {sentiment, specific_praise}

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2. The Logic of "Indirect Recommendation"

Most systems look for 1-hop links: (User)-[:BOUGHT]->(Product). Graph RAG looks for Multimodal Path Reasoning:

1. (User) likes (Gaming).
2. (Gaming) requires (Low Latency).
3. (Monitor A) has the (Low Latency) attribute and is (Highly Rated) by other (Gaming) users.
4. Recommendation: "I recommend Monitor A because it meets your Latency requirement for Gaming."

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3. Explainability: The "Reasoning" Context

When the AI agent retrieves a path for recommendation, it uses the Arrow Format (Module 9) as context.

• AI Prompt: "The user likes X. Here is the path to Product Y: (X)--[NEEDS]-->[Feature Z]<--[HAS]--(Y). Write a persuasive recommendation."

The Narrative Answer: "Since you enjoy nature photography, you'll love this lens because it has the weather-sealing you need for outdoor shoots, and it's compatible with the Z-mount camera you already own."

graph LR
    U[User] -->|Preferences| A[Nature Photo]
    A -->|Requires| S[Weather Seal]
    L[Lens 400mm] -->|Has| S
    L -->|Compatible| C[Camera Z6]
    U -->|Owns| C
    
    subgraph "Reasoning Path"
    U --- A --- S --- L --- C --- U
    end
    
    style U fill:#4285F4,color:#fff
    style L fill:#34A853,color:#fff

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4. Implementation: A "Justified Recommendation" in Cypher

MATCH (u:User \{id: $id\})-[:HAS_INTEREST]->(i:Interest)
MATCH (p:Product)-[:SUPPORTS]->(i)
MATCH (p)-[:HAS_FEATURE]->(f:Feature)
WHERE f.name IN u.needed_features

RETURN p.name as product, 
       collect(f.name) as matching_features, 
       i.name as shared_interest;

// The AI uses this data to say: "Product P supports your 
// interest in I and has the specific features F you need."

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5. Summary and Exercises

Reasoning-based recommendation turns a "Sale" into a "Consultation."

• Attribute Linking finds the "Functional" match.
• Path Serialization provides the "Reasoning."
• Multimodal Connections (Reviews + Specs + History) provide the "Evidence."
• Transparency leads to higher conversions.

Exercises

1. Justification Task: A user likes "Science Fiction." Suggest a book about "Robots" using a 3-hop graph path. What is the bridge? (e.g., Isaac Asimov).
2. The "Anti-Recommendation": How would you use a graph edge like [:CONFLICTS_WITH] to stop the AI from recommending a product that doesn't work with the user's current gear?
3. Visualization: Draw a graph linking "You" to a "Vacuum Cleaner." Use a "Pet Hair" node as the bridge.

In the next lesson, we will look at a physical world use case: Supply Chain and Logistics Optimization.