  The main contribution of this paper is to introduce and describe a new
recommender-systems dataset (RARD II). It is based on data from Mr. DLib, a
recommender-system as-a-service in the digital library and
reference-management-software domain. As such, RARD II complements datasets
from other domains such as books, movies, and music. The dataset encompasses
94m recommendations, delivered in the two years from September 2016 to
September 2018. The dataset covers an item-space of 24m unique items. RARD II
provides a range of rich recommendation data, beyond conventional ratings. For
example, in addition to the usual (implicit) ratings matrices, RARD II includes
the original recommendation logs, which provide a unique insight into many
aspects of the algorithms that generated the recommendations. The logs enable
researchers to conduct various analyses about a real-world recommender system.
This includes the evaluation of meta-learning approaches for predicting
algorithm performance. In this paper, we summarise the key features of this
dataset release, describe how it was generated and discuss some of its unique
features. Compared to its predecessor RARD, RARD II contains 64% more
recommendations, 187% more features (algorithms, parameters, and statistics),
50% more clicks, 140% more documents, and one additional service partner
(JabRef).
