  In the evolving field of personalized news recommendation, understanding the
semantics of the underlying data is crucial. Large Language Models (LLMs) like
GPT-4 have shown promising performance in understanding natural language.
However, the extent of their applicability in news recommendation systems
remains to be validated. This paper introduces RecPrompt, the first framework
for news recommendation that leverages the capabilities of LLMs through prompt
engineering. This system incorporates a prompt optimizer that applies an
iterative bootstrapping process, enhancing the LLM-based recommender's ability
to align news content with user preferences and interests more effectively.
Moreover, this study offers insights into the effective use of LLMs in news
recommendation, emphasizing both the advantages and the challenges of
incorporating LLMs into recommendation systems.
