  Large Language Models (LLMs) have achieved remarkable progress in language
understanding and generation. Custom LLMs leveraging textual features have been
applied to recommendation systems, demonstrating improvements across various
recommendation scenarios. However, most existing methods perform untrained
recommendation based on pre-trained knowledge (e.g., movie recommendation), and
the auto-regressive generation of LLMs leads to slow inference speeds, making
them less effective in real-time recommendations.To address this, we propose a
framework for news recommendation using LLMs, named \textit{CherryRec}, which
ensures the quality of recommendations while accelerating the recommendation
process. Specifically, we employ a Knowledge-aware News Rapid Selector to
retrieve candidate options based on the user's interaction history. The history
and retrieved items are then input as text into a fine-tuned LLM, the
Content-aware News Llm Evaluator, designed to enhance news recommendation
capabilities. Finally, the Value-aware News Scorer integrates the scores to
compute the CherryRec Score, which serves as the basis for the final
recommendation.We validate the effectiveness of the proposed framework by
comparing it with state-of-the-art baseline methods on benchmark datasets. Our
experimental results consistently show that CherryRec outperforms the baselines
in both recommendation performance and efficiency.The project resource can be
accessed at: \url{https://github.com/xxxxxx}
