RARD II: The 2nd Related-Article Recommendation Dataset

We introduce RARD II: The 2nd Related-Article Recommendation Dataset from the recommendation-as-a-service provider Mr. DLib (http://mr-dlib.org). The RARD II dataset encompasses 94m recommendations, covering an item-space of 24m unique items. RARD II provides a range of rich recommendation data, beyond conventional ratings. 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. Compared to its predecessor RARD I, RARD II contains 64% more recommendations, 187% more features (algorithms, parameters, and statistics), 50% more clicks, 140% more documents, and in addition to Sowiport, adds another service partner (i.e. JabRef). RARD II 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. RARD II is a unique dataset with high value to recommender-systems researchers, particularly in the domain of digital libraries.