Stereotype and Most-Popular Recommendations in the Digital Library Sowiport [Dataset]

Name: Stereotype and Most-Popular Recommendations in the Digital Library Sowiport [Dataset]
Creator: National Institute of Informatics; Birla Institute for Technology and Science, India; Trinity College Dublin, School of Computer Science and Statistics, ADAPT Centre; GESIS – Leibniz Institute for the Social Sciences
Published: 2017-01-01T00:00:00+08:00

Harvard Dataverse2017-06-10 更新2026-04-09 收录

数字图书馆推荐系统

算法评估

数据链接：

https://dataverse.harvard.edu/citation?persistentId=doi:10.7910/DVN/HFIV1A 数据链接链接失效反馈

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资源简介：

This data relates to our paper "Stereotype and Most-Popular Recommendations in the Digital Library Sowiport". The data includes a list of the 28 million delivered and clicked recommendations as CSV file, the R script to analyze the data, and the figures and tables presented in this paper as PNG and CSV files. This open access to the data allows replicating our analyses, checking the results for correctness, and conducting additional analyses.

提供机构：

National Institute of Informatics; Birla Institute for Technology and Science, India; Trinity College Dublin, School of Computer Science and Statistics, ADAPT Centre; GESIS – Leibniz Institute for the Social Sciences

创建时间：

2017-01-01