Authors: Samruddhi Maheshkumar Aher, Harshali Rajendra Bagul, Diksha Ravindra Nirbhavane, Ashwini Nandu Pawar, Puneet Eknath Patel
Abstract: E-commerce platforms generate millions of product listings, often causing information overload and generic, non-personalized suggestions. Traditional recommendation systems operate as black boxes, resulting in limited user trust due to the lack of transparency. This paper proposes an AI-driven Explainable Product Recommendation System integrating Large. Language Models (LLaMA-2), FAISS semantic search, and SHAP-based interpretability. The system processes natural language queries, interprets intent, retrieves relevant products across multiple platforms, and generates human-readable explanations. Experimental evaluation demonstrates improved accuracy, transparency, and user satisfaction compared to traditional recommendation approaches.
DOI: http://doi.org/10.5281/zenodo.20766113
