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.

通过引用语句，建立一个可靠的新闻信息来源推荐体系，以提高推荐系统的预测和行为质量。

利用大型语言模型进行新闻源推荐的多层排名

Multi-Layer Ranking with Large Language Models for News Source  Recommendation

To enhance the ability to find credible evidence in news articles, we propose
a novel task of expert recommendation, which aims to identify trustworthy
experts on a specific news topic. To achieve the aim, we describe the
construction of a novel NewsQuote dataset consisting of 24,031 quote-speaker
pairs that appeared on a COVID-19 news corpus. We demonstrate an automatic
pipeline for speaker and quote extraction via a BERT-based Question Answering
model. Then, we formulate expert recommendations as document retrieval task by
retrieving relevant quotes first as an intermediate step for expert
identification, and expert retrieval by directly retrieving sources based on
the probability of a query conditional on a candidate expert. Experimental
results on NewsQuote show that document retrieval is more effective in
identifying relevant experts for a given news topic compared to expert
retrieval

本论文提出了一种基于相关性的新闻专家选择任务，通过构建一个包括 24,031 对引语 - 发言人的新闻引文数据集，采用基于 BERT 的问答模型对其进行自动抽取，然后通过文件检索任务鉴定信任专家。实验证明，与专家检索相比，文件检索可以更有效地识别与特定新闻主题相关的专家。

基于引文提取和归属的新闻数据集，用于事实核查专家推荐

NewsQuote: A Dataset Built on Quote Extraction and Attribution for  Expert Recommendation in Fact-Checking

Community question answering (CQA) represents the type of Web applications
where people can exchange knowledge via asking and answering questions. One
significant challenge of most real-world CQA systems is the lack of effective
matching between questions and the potential good answerers, which adversely
affects the efficient knowledge acquisition and circulation. On the one hand, a
requester might experience many low-quality answers without receiving a quality
response in a brief time, on the other hand, an answerer might face numerous
new questions without being able to identify their questions of interest
quickly. Under this situation, expert recommendation emerges as a promising
technique to address the above issues. Instead of passively waiting for users
to browse and find their questions of interest, an expert recommendation method
raises the attention of users to the appropriate questions actively and
promptly. The past few years have witnessed considerable efforts that address
the expert recommendation problem from different perspectives. These methods
all have their issues that need to be resolved before the advantages of expert
recommendation can be fully embraced. In this survey, we first present an
overview of the research efforts and state-of-the-art techniques for the expert
recommendation in CQA. We next summarize and compare the existing methods
concerning their advantages and shortcomings, followed by discussing the open
issues and future research directions.

本文概述了社区问答中专家推荐的研究进展和现有技术，总结并比较了现有方法的优缺点，并讨论了未来研究方向和面临的挑战。