CherryRec: Enhancing News Recommendation Quality via LLM-driven Framework

TL;DR

CherryRec is a novel framework that leverages fine-tuned LLMs and knowledge-aware retrieval to improve news recommendation quality and speed, outperforming existing methods on benchmark datasets.

Contribution

Introduces CherryRec, a framework combining knowledge-aware retrieval and fine-tuned LLMs to enhance news recommendation accuracy and efficiency.

Findings

CherryRec outperforms baseline methods in recommendation accuracy.

CherryRec demonstrates improved efficiency over traditional LLM-based approaches.

Experimental results validate the effectiveness of the proposed framework.

Abstract

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}