Transforming unstructured natural language requests into high-conversion, business-aligned product recommendations.
Navigating vast e-commerce inventories with rigid filter tags often falls short of capturing true consumer intent. The Shopping Product Recommendation Assistant bridges this gap by turning natural, conversational requests (e.g., "32-inch TV under $500 with great picture quality") into precise, structured search parameters.
By unifying semantic retrieval (Vector Embeddings), fine-tuned LLM parsing, and business-driven ranking metrics (such as margin optimization and inventory levels), this engine delivers grounded, high-relevance recommendations with transparent explanations.
- 🧠 Intent-to-Query LLM: Fine-tuned model that parses unstructured user prompts into structured JSON query parameters.
- 🔍 Hybrid Vector Retrieval: Context-aware semantic search powered by custom vector embeddings and product metadata.
- 📈 Strategic Business Ranking: Multi-factor scoring engine that balances consumer relevance with real-world inventory and margin goals.
- 💬 Grounded Explainability: Generates clear, human-readable explanations detailing why each product fits the user's explicit criteria.