Comparison of traditional machine learning algorithms with large language models for developing personalized recommender systems for enhancing passenger experience on flights
This thesis compares traditional machine learning algorithms and large language models (LLMs) in developing personalized recommender systems to enhance passenger experiences on flights. Traditional methods like collaborative filtering have been widely used but face challenges such as data sparsity and cold-start problems. LLMs, with their advanced natural language processing capabilities, offer pr
