Advancements in Context-Aware Recommender Systems: An Exhaustive Exploration of Ontology and LLM in improving ranking accuracy

Recommendation systems have become a ubiquitous presence across various domains such as e-commerce, entertainment (movies and music), tourism, news, advertising, stock markets and social networks. The integration of Ontology and Large Language Models (LLMs) presents a transformative approach for enhancing recommender systems by improving contextual intelligence, personalization, and explainability. Traditional recommendation algorithms often suffer from data sparsity, cold-start problems, and limited semantic understanding, which hinder their ability to deliver highly relevant and interpretable suggestions. This research provides an exhaustive exploration of how ontology-driven knowledge representation can be seamlessly combined with LLMs to refine user-item interactions, enrich embeddings, and improve reasoning in recommendation pipelines. We propose a hybrid framework where ontological structures encode domain-specific relationships while LLMs process, infer, and enhance recommendations through contextual embeddings.This synergy enables semantic-aware recommendations, explainable item suggestions, and adaptive learning mechanisms that adjust based on user behavior and external context. Furthermore, we investigate novel ontology injection techniques, including knowledge graph-based reinforcement, to improve long-tail item exposure and enhance recommendation diversity. Through rigorous evaluation on real-world datasets, we demonstrated how ontology-LLM fusion significantly outperforms baseline models in accuracy, novelty, and interpretability. This study establishes a foundation for the next generation of context-aware, knowledge-enhanced recommender systems that leverage LLMs for reasoning and ontologies for structured knowledge representation, paving the way for more intelligent and user-centric recommendations.