RARD: The Related-Article Recommendation Dataset [Data]

We introduce RARD, the Related-Article Recommendation Dataset, from the digital library Sowiport and the recommendation-as-a-service provider Mr. DLib (http://mr-dlib.org). The dataset contains information about 57.4 million recommendations that were displayed to the users of Sowiport. Information includes details on which recommendation approaches were used (e.g. content-based filtering, stereotype, most popular), what types of features were used in content based filtering (simple terms vs. keyphrases), where the features were extracted from (title or abstract), and the time when recommendations were delivered and clicked. In addition, the dataset contains an implicit item-item rating matrix that was created based on the recommendation click logs. RARD enables researchers to train machine learning algorithms for research-paper recommendations, perform offline evaluations, and do research on data from Mr. DLib’s recommender system, without implementing a recommender system themselves. In the field of scientific recommender systems, our dataset is unique. To the best of our knowledge, there is no dataset with more (implicit) ratings available, and that many variations of recommendation algorithms. The dataset is available at http://data.mr-dlib.org, and published under the “Creative Commons Attribution 3.0 Unported (CC-BY)” license.本研究提出RARD——相关文章推荐数据集（Related-Article Recommendation Dataset），其源自数字图书馆Sowiport与推荐即服务提供商Mr. DLib（http://mr-dlib.org）。该数据集包含面向Sowiport用户展示的5740万条推荐记录相关信息，涵盖所采用的推荐方法细节（如基于内容的过滤（content-based filtering）、刻板偏好推荐、热门物品推荐）、基于内容过滤时所使用的特征类型（简单词元 vs 关键词短语）、特征提取来源（标题或摘要），以及推荐推送与点击的时间戳。此外，该数据集还包含基于推荐点击日志构建的隐式物品-物品评分矩阵。RARD可帮助研究人员无需自行搭建推荐系统，即可训练用于学术论文推荐的机器学习算法、开展离线评估，以及针对Mr. DLib推荐系统的相关数据进行研究。在学术推荐系统领域，本数据集具有独特性。据我们所知，目前尚无公开数据集拥有如此多的（隐式）评分条目，且涵盖如此丰富的推荐算法变体。该数据集可通过http://data.mr-dlib.org获取，并采用“知识共享署名3.0未移植版（Creative Commons Attribution 3.0 Unported，CC-BY）”许可协议发布。