  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.
