15 citations · 46 across the 11 of their papers we have counts for
4 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,…
Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) Optimization
Rohan Anand, Joeran Beel
We introduce Auto-Surprise, an Automated Recommender System library. Auto-Surprise is an extension of the Surprise recommender system library and eases the algorithm selection and…
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…