Recommendation systems with LLM-based semantic embeddings and FAISS similarity search

Content-based recommendation systems have gained significant attention for their ability to provide personalized suggestions by analyzing item descriptions. Leveraging the power of large language models (LLMs), this research introduces a novel recommendation approach that generates high-quality semantic embeddings to facilitate efficient similarity-based retrieval for Top-N recommendations. The proposed method capitalizes on the deep contextual understanding of LLMs to capture intricate semantic relationships within item content, thereby enhancing recommendation relevance. Furthermore, the system integrates FAISS (Facebook AI Similarity Search) to optimize similarity search, enabling faster and more scalable retrieval of relevant recommendations. To evaluate its effectiveness, the system is tested on four diverse real-world datasets: Yelp, Amazon Beauty, MovieLens, and LastFM, covering multiple domains. Performance is assessed using widely adopted evaluation metrics, including Normalized Discounted Cumulative Gain (NDCG), Precision, Recall, Hit Rate (HR), F1-Score and business-relevant evaluation measures. Extensive experimental results demonstrate that the proposed method, augmented with FAISS, consistently outperforms the existing state-of-the-art recommendation techniques. The code supporting this code is publicly available at: https://github.com/seemasafar/Reco-System-Using-LLM