Context-Aware Tourism Recommendations Using Retrieval-Augmented Large Language Models and Semantic Re-Ranking

This study evaluates the performance of seven large language models (LLMs) in generating context-aware recommendations. The system is built on a collection of PDF documents (brochures) describing local events and activities, which are embedded into an FAISS vector store to support semantic retrieval. Synthetic user profiles are defined to simulate diverse preferences, while static weather conditions are incorporated to enhance the contextual relevance of recommendations. To further improve output quality, a reranking step, utilizing Cohere’s API, is used to refine the top retrieved results before passing them to the LLMs for final response generation. This allows better semantic organization of relevant content in line with user context. The main aim of this research is to identify which models best integrate multimodal inputs, such as user intent, profile attributes, environmental context and how these insights can inform the development of adaptive, personalized recommendation systems. The main contribution of this study is a structured comparative analysis of 7 LLMs, applied to a tourism-specific RAG framework, providing practical insights into how effectively different models integrate contextual factors to produce personalized recommendations. The evaluation revealed notable differences in model performance, with Qwen and Phi emerging as the strongest performers, whereas LLaMA frequently produced irrelevant recommendations. Moreover, many models favored gastronomy-related venues over other types of attractions. These findings indicate that although the RAG framework provides a solid foundation, the selection of underlying models plays an important role in achieving high quality recommendations.