11 citations · 49 across the 12 of their papers we have counts for
7 papers · 1 filter
The Architecture of Mr. DLib's Scientific Recommender-System API
Joeran Beel, Andrew Collins, Akiko Aizawa
Recommender systems in academia are not widely available. This may be in part due to the difficulty and cost of developing and maintaining recommender systems. Many operators of ac…
Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations
Joeran Beel, Andrew Collins, Oliver Kopp +2
We introduce the first 'living lab' for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for sc…
RARD II: The 94 Million Related-Article Recommendation Dataset
Joeran Beel, Barry Smyth, Andrew Collins
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 i…
One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level
Andrew Collins, Dominika Tkaczyk, Joeran Beel
The effectiveness of recommendation algorithms is typically assessed with evaluation metrics such as root mean square error, F1, or click through rates, calculated over entire data…
A Study of Position Bias in Digital Library Recommender Systems
Andrew Collins, Dominika Tkaczyk, Akiko Aizawa +1
"Position bias" describes the tendency of users to interact with items on top of a list with higher probability than with items at a lower position in the list, regardless of the i…
A Method for Discovering and Extracting Author Contributions Information from Scientific Biomedical Publications
Dominika Tkaczyk, Andrew Collins, Joeran Beel
Creating scientific publications is a complex process, typically composed of a number of different activities, such as designing the experiments, data preparation, programming soft…