17 citations · 17 across the 2 of their papers we have counts for
9 papers · 1 filter
ParsRec: A Novel Meta-Learning Approach to Recommending Bibliographic Reference Parsers
Dominika Tkaczyk, Rohit Gupta, Riccardo Cinti +1
Bibliographic reference parsers extract machine-readable metadata such as author names, title, journal, and year from bibliographic reference strings. To extract the metadata, the…
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…
ParsRec: Meta-Learning Recommendations for Bibliographic Reference Parsing
Dominika Tkaczyk, Paraic Sheridan, Joeran Beel
Bibliographic reference parsers extract metadata (e.g. author names, title, year) from bibliographic reference strings. No reference parser consistently gives the best results in e…
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…