11 citations · 49 across the 12 of their papers we have counts for
12 papers
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,…
Siamese Meta-Learning and Algorithm Selection with 'Algorithm-Performance Personas' [Proposal]
Joeran Beel, Bryan Tyrell, Edward Bergman +2
Automated per-instance algorithm selection often outperforms single learners. Key to algorithm selection via meta-learning is often the (meta) features, which sometimes though do n…
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…
NaïveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts
Dominika Tkaczyk, Andrew Collins, Joeran Beel
Information about the contributions of individual authors to scientific publications is important for assessing authors' achievements. Some biomedical publications have a short sec…
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…