Improving Recommender Systems with Large Language Models
Sebastian Lubos · 2024
Recommender systems offer an important technology aiding users in discovering relevant items among a multitude of available options. The emergence of Large Language Models (LLMs) enables a powerful opportunity to improve the performance and flexibility of recommender systems in different aspects. Those involve possibilities to enhance the item retrieval and ranking, explanation of recommendations, and generation of content to be recommended. The objective of my Ph.D. is to investigate those possibilities to identify and develop methods for improving different aspects of recommender systems using LLMs. The findings are expected to further develop the field of recommender systems and reveal novel opportunities for future research.