Rating-based collaborative filtering: algorithms and evaluation
Recommender systems help users find information by recommending content that a user
might not know about, but will hopefully like. Rating-based collaborative filtering …
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
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 …
From the several open problems in this area, the decision of which is the best …
Sequential scenario-specific meta learner for online recommendation
Cold-start problems are long-standing challenges for practical recommendations. Most
existing recommendation algorithms rely on extensive observed data and are brittle to …
existing recommendation algorithms rely on extensive observed data and are brittle to …
Letting users choose recommender algorithms: An experimental study
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 …
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
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 …
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
Recommender systems often face heterogeneous datasets containing highly personalized
historical data of users, where no single model could give the best recommendation for …
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 …
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 …
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 …
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 …
community since the early 1990s. Collaborative filtering (and other) algorithms, however …