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Found 8 papers, 2 papers with code
KGUF: Simple Knowledge-aware Graph-based Recommender with User-based Semantic Features Filtering
The recent integration of Graph Neural Networks (GNNs) into recommendation has led to a novel family of Collaborative Filtering (CF) approaches, namely Graph Collaborative Filtering (GCF).
Beyond Demographic Parity: Redefining Equal Treatment
Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics.
Sparse Feature Factorization for Recommender Systems with Knowledge Graphs
In fact, in these cases we have that with a large number of high-quality features, the resulting models are more complex and difficult to train.
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations.
FedeRank: User Controlled Feedback with Federated Recommender Systems
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life.
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a vast space of possible choices.
Prioritized Multi-Criteria Federated Learning
In order to address these issues, Federated Learning (FL) has been recently proposed as a means to build ML models based on private datasets distributed over a large number of clients, while preventing data leakage.
Towards Effective Device-Aware Federated Learning
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in terms of users' personal privacy and data security.
