HEMANGANI/LLM-Recommendation-Systems
This project fine-tunes large language models (LLMs) for text-based recommendations, using a novel prompt mechanism to improve accuracy and user satisfaction. It demonstrates efficient model adaptation with diverse datasets, leveraging advanced libraries and techniques for optimal performance.
This project helps e-commerce managers and product strategists improve how they suggest products to customers. By feeding detailed customer review data and past purchase history into a specialized AI, it learns to recommend the next most relevant product. It’s for anyone who needs to make highly accurate, text-based product recommendations.
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Use this if you manage an online store or platform and want to predict customer purchases more accurately using their past review and interaction data.
Not ideal if your recommendation needs are not text-based or if you lack detailed customer review and interaction data.
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Sep 08, 2024
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