An Explainable Retrieval-Augmented Generation-Based Personalized Recommendation System
Recommendation engines are significant tools that provide personalized recommendations especially in case of cold start problem where historical interaction data is insufficient. Deep learning techniques such as the state-of-the-art RAG (Retrieval-Augmented Generation) method make the recommendation procedure better by utilizing the concept of contextual preference. They, however, depend on large language models and therefore it increases the computational cost, slow the process and make it less explainable. This paper discusses a lightweight and explainable RAG-based recommendation engine. In the proposed model, SBERT embeddings and FAISS indexing are used for effective semantic retrieval and hybrid ranking. Explainability component is included in the architecture to provide explanations using similarity score, rating, popularity, author similarity and publication era. Experiments are performed on Kaggle Book-Crossing dataset using ranking metrics such as Recall, NDCG and MRR at various K values, where K is the number of recommendations selected from the ranked list. As per the experimental results, the proposed framework achieves its highest MRR score of 0.1509 at K = 25.