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.
@article{arxiv.2406.11745,
title = {Multi-Layer Ranking with Large Language Models for News Source Recommendation},
author = {Wenjia Zhang and Lin Gui and Rob Procter and Yulan He},
journal= {arXiv preprint arXiv:2406.11745},
year = {2024}
}
Comments
Accepted by the SIGIR 2024. arXiv admin note: text overlap with arXiv:2305.04825