5 citations · 10 across the 5 of their papers we have counts for
7 papers · 1 filter
Per-Instance Algorithm Selection for Recommender Systems via Instance Clustering
Andrew Collins, Laura Tierney, Joeran Beel
Recommendation algorithms perform differently if the users, recommendation contexts, applications, and user interfaces vary even slightly. It is similarly observed in other fields,…
Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems
Andrew Collins, Joeran Beel
The effectiveness of recommender system algorithms varies in different real-world scenarios. It is difficult to choose a best algorithm for a scenario due to the quantity of algori…
Document Embeddings vs. Keyphrases vs. Terms: An Online Evaluation in Digital Library Recommender Systems
Andrew Collins, Joeran Beel
Many recommendation algorithms are available to digital library recommender system operators. The effectiveness of algorithms is largely unreported by way of online evaluation. We…
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…