Rating-based collaborative filtering: algorithms and evaluation

D Kluver, MD Ekstrand, JA Konstan - Social information access: Systems …, 2018 - Springer
Recommender systems help users find information by recommending content that a user
might not know about, but will hopefully like. Rating-based collaborative filtering …

Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering

T Cunha, C Soares, AC de Carvalho - Information Sciences, 2018 - Elsevier
The problem of information overload motivated the appearance of Recommender Systems.
From the several open problems in this area, the decision of which is the best …

Sequential scenario-specific meta learner for online recommendation

Z Du, X Wang, H Yang, J Zhou, J Tang - Proceedings of the 25th ACM …, 2019 - dl.acm.org
Cold-start problems are long-standing challenges for practical recommendations. Most
existing recommendation algorithms rely on extensive observed data and are brittle to …

Letting users choose recommender algorithms: An experimental study

MD Ekstrand, D Kluver, FM Harper… - Proceedings of the 9th …, 2015 - dl.acm.org
Recommender systems are not one-size-fits-all; different algorithms and data sources have
different strengths, making them a better or worse fit for different users and use cases. As …

Operationalizing the legal principle of data minimization for personalization

AJ Biega, P Potash, H Daumé, F Diaz… - Proceedings of the 43rd …, 2020 - dl.acm.org
Article 5 (1)(c) of the European Union's General Data Protection Regulation (GDPR)
requires that" personal data shall be [...] adequate, relevant, and limited to what is necessary …

Metaselector: Meta-learning for recommendation with user-level adaptive model selection

M Luo, F Chen, P Cheng, Z Dong, X He… - Proceedings of The Web …, 2020 - dl.acm.org
Recommender systems often face heterogeneous datasets containing highly personalized
historical data of users, where no single model could give the best recommendation for …

A new data characterization for selecting clustering algorithms using meta-learning

BA Pimentel, AC De Carvalho - Information Sciences, 2019 - Elsevier
Meta-learning has been successfully used for algorithm recommendation tasks. It uses
machine learning to induce meta-models able to predict the best algorithms for a new …

Evaluating recommender behavior for new users

D Kluver, JA Konstan - Proceedings of the 8th ACM Conference on …, 2014 - dl.acm.org
The new user experience is one of the important problems in recommender systems. Past
work on recommending for new users has focused on the process of gathering information …

A multi-armed bandit model selection for cold-start user recommendation

CZ Felício, KVR Paixão, CAZ Barcelos… - Proceedings of the 25th …, 2017 - dl.acm.org
How can we effectively recommend items to a user about whom we have no information?
This is the problem we focus on in this paper, known as the cold-start problem. In most …

Collaborative filtering algorithms are prone to mainstream-taste bias

P Pipergias Analytis, P Hager - … of the 17th ACM Conference on …, 2023 - dl.acm.org
Collaborative filtering has been a dominant approach in the recommender systems
community since the early 1990s. Collaborative filtering (and other) algorithms, however …