PaleoRec: A sequential recommender system for the annotation of paleoclimate datasets

PaleoRec: A sequential recommender system for the annotation of paleoclimate datasets
复制标题

DOI:
10.1017/eds.2022.3
复制
发表时间:
2022-04
期刊:
Environmental Data Science
影响因子:
--
通讯作者:
Shravya Manety;D. Khider;Christopher Heiser;N. McKay;J. Emile‐Geay;C. Routson
Shravya Manety;D. Khider;Christopher Heiser;N. McKay;J. Emile‐Geay;C. Routson
中科院分区:
其他
文献类型:
--
作者:
Shravya Manety;D. Khider;Christopher Heiser;N. McKay;J. Emile‐Geay;C. Routson

文献摘要

相似文献

研究过去的气候变率是我们理解当前气候变化的基础。在大数据时代,古气候信息的价值关键取决于我们对大量数据的分析能力,而分析能力本身就取决于标准化。标准化还确保这些数据集更易于查找、访问、互操作和可重用。在古气候学界努力标准化古气候数据的格式、术语和报告的基础上,本文描述了PaleoRec,一个用于注释这些数据集的推荐系统。其目标是通过在下拉菜单中减少和排列相关条目来帮助科学家完成注释任务。科学家可以为他们的元数据选择最好的选项,也可以手动输入适当的信息。PaleoRec旨在减少科学研究的时间,同时确保遵守社区标准。PaleoRec是一种基于递归神经网络的顺序推荐系统,它考虑了用户对特定数据集的短期兴趣。该模型使用1996年专家注释的数据集开发,产生6512个序列。该算法的性能,以命中率衡量,在0.7和1.0之间变化。PaleoRec目前部署在一个web界面上,用于使用新兴社区标准对古气候数据集进行注释。
Abstract Studying past climate variability is fundamental to our understanding of current changes. In the era of Big Data, the value of paleoclimate information critically depends on our ability to analyze large volume of data, which itself hinges on standardization. Standardization also ensures that these datasets are more Findable, Accessible, Interoperable, and Reusable. Building upon efforts from the paleoclimate community to standardize the format, terminology, and reporting of paleoclimate data, this article describes PaleoRec, a recommender system for the annotation of such datasets. The goal is to assist scientists in the annotation task by reducing and ranking relevant entries in a drop-down menu. Scientists can either choose the best option for their metadata or enter the appropriate information manually. PaleoRec aims to reduce the time to science while ensuring adherence to community standards. PaleoRec is a type of sequential recommender system based on a recurrent neural network that takes into consideration the short-term interest of a user in a particular dataset. The model was developed using 1996 expert-annotated datasets, resulting in 6,512 sequences. The performance of the algorithm, as measured by the Hit Ratio, varies between 0.7 and 1.0. PaleoRec is currently deployed on a web interface used for the annotation of paleoclimate datasets using emerging community standards.