Informed Dataset Selection with 'Algorithm Performance Spaces'
When designing recommender-systems experiments, a key question that has been largely
overlooked is the choice of datasets. In a brief survey of ACM RecSys papers, we found that …
overlooked is the choice of datasets. In a brief survey of ACM RecSys papers, we found that …
Per-instance algorithm selection for recommender systems via instance clustering
Recommendation algorithms perform differently if the users, recommendation contexts,
applications, and user interfaces vary even slightly. It is similarly observed in other fields …
applications, and user interfaces vary even slightly. It is similarly observed in other fields …
Rethinking Recommender Systems: Cluster-based Algorithm Selection
A Lizenberger, F Pfeifer, B Polewka - arXiv preprint arXiv:2405.18011, 2024 - arxiv.org
Cluster-based algorithm selection deals with selecting recommendation algorithms on
clusters of users to obtain performance gains. No studies have been attempted for many …
clusters of users to obtain performance gains. No studies have been attempted for many …
[HTML][HTML] A first analysis of meta-learned per-instance algorithm selection in scholarly recommender systems
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 algorithms available …
difficult to choose a best algorithm for a scenario due to the quantity of algorithms available …
[PDF][PDF] Preface: The 1st Interdisciplinary Workshop on Algorithm Selection and Meta-Learning in Information Retrieval (AMIR).
J Beel, L Kotthoff - AMIR@ ECIR, 2019 - ceur-ws.org
Algorithm selection is a key challenge for most, if not all, computational problems. Typically,
there are several potential algorithms that can solve a problem, but which algorithm would …
there are several potential algorithms that can solve a problem, but which algorithm would …
[PDF][PDF] Federated meta-learning: Democratizing algorithm selection across disciplines and software libraries
J Beel - Science (AICS), 2018 - researchgate.net
There is an ever-growing number of tools for automating the machine learning pipeline, both
commercial and open source. Auto-sklearn [11, 15], Auto Weka [14], ML-Plan [18], and H2O …
commercial and open source. Auto-sklearn [11, 15], Auto Weka [14], ML-Plan [18], and H2O …
Meta-learned per-instance algorithm selection in scholarly recommender systems
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 …
scenarios. It is difficult to choose a best algorithm for a scenario due to the quantity of …