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Found 19 papers, 10 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).
Formalizing Multimedia Recommendation through Multimodal Deep Learning
Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains.
Evaluating ChatGPT as a Recommender System: A Rigorous Approach
Through thoroughly exploring ChatGPT's abilities in recommender systems, our study aims to contribute to the growing body of research on the versatility and potential applications of large language models.
A Topology-aware Analysis of Graph Collaborative Filtering
The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF).
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
The success of graph neural network-based models (GNNs) has significantly advanced recommender systems by effectively modeling users and items as a bipartite, undirected graph.
Machine-learned Adversarial Attacks against Fault Prediction Systems in Smart Electrical Grids
In smart electrical grids, fault detection tasks may have a high impact on society due to their economic and critical implications.
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting
Our experiments show that, even if the model is trained without sensitive features, it often suffers discriminatory biases.
Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning
The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behavior and, in light of recent regulations, has attracted the attention of the research community.
MAFUS: a Framework to predict mortality risk in MAFLD subjects
Few articles are focused on mortality in MAFLD subjects, and none investigate how to predict a fatal outcome.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Interactive Question Answering Systems: Literature Review
Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems.
URANUS: Radio Frequency Tracking, Classification and Identification of Unmanned Aircraft Vehicles
A cost-effective and real-time framework is needed to detect the presence of drones in such cases.
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.
How to make latent factors interpretable by feeding Factorization machines with knowledge graphs
By relying on the information encoded in the original knowledge graph, we have also evaluated the semantic accuracy and robustness for the knowledge-aware interpretability of the final model.
On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them
Hyper-parameters tuning is a crucial task to make a model perform at its best.
Knowledge-aware Autoencoders for Explainable Recommender Sytems
Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem.
Computing recommendations via a Knowledge Graph-aware Autoencoder
In the last years, deep learning has shown to be a game-changing technology in artificial intelligence thanks to the numerous successes it reached in diverse application fields.
Local Popularity and Time in top-N Recommendation
Items popularity is a strong signal in recommendation algorithms.
The importance of being dissimilar in Recommendation
Similarity measures play a fundamental role in memory-based nearest neighbors approaches.
Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios
In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning.
