Multi-Layer Ranking with Large Language Models for News Source Recommendation
<|reference_start|>Multi-Layer Ranking with Large Language Models for News Source Recommendation: To seek reliable information sources for news events, we introduce a novel task of expert recommendation, which aims to identify trustworthy sources based on their previously quoted statements. To achieve this, we built a novel dataset, called NewsQuote, consisting of 23,571 quote-speaker pairs sourced from a collection of news articles. We formulate the recommendation task as the retrieval of experts based on their likelihood of being associated with a given query. We also propose a multi-layer ranking framework employing Large Language Models to improve the recommendation performance. Our results show that employing an in-context learning based LLM ranker and a multi-layer ranking-based filter significantly improve both the predictive quality and behavioural quality of the recommender system.<|reference_end|>
@article{zhang2024multi-layer,
    title={Multi-Layer Ranking with Large Language Models for News Source
  Recommendation},
    author={Wenjia Zhang, Lin Gui, Rob Procter, Yulan He},
    journal={arXiv preprint arXiv:2406.11745},
    year={2024},
    doi={10.1145/3626772.3657966},
    archivePrefix={arXiv},
    eprint={2406.11745},
    primaryClass={cs.IR cs.CL}
}