research
∙
09/07/2023
Evaluating ChatGPT as a Recommender System: A Rigorous Approach
Recent popularity surrounds large AI language models due to their impres...
share
research
∙
08/21/2023
A Topology-aware Analysis of Graph Collaborative Filtering
The successful integration of graph neural networks into recommender sys...
research
∙
08/01/2023
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
The success of graph neural network-based models (GNNs) has significantl...
research
∙
06/21/2023
Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation
Information Retrieval (IR) and Recommender Systems (RS) tasks are moving...
research
∙
02/16/2023
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting
Current AI regulations require discarding sensitive features (e.g., gend...
research
∙
02/16/2023
Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning
The increasing application of Artificial Intelligence and Machine Learni...
research
∙
03/02/2022
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art
Research on recommender systems algorithms, like other areas of applied ...
research
∙
09/02/2021
Adherence and Constancy in LIME-RS Explanations for Recommendation
Explainable Recommendation has attracted a lot of attention due to a ren...
research
∙
07/29/2021
Sparse Feature Factorization for Recommender Systems with Knowledge Graphs
Deep Learning and factorization-based collaborative filtering recommenda...
research
∙
07/29/2021
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
Recommender systems (RSs) employ user-item feedback, e.g., ratings, to m...
research
∙
07/28/2021
Reenvisioning Collaborative Filtering vs Matrix Factorization
Collaborative filtering models based on matrix factorization and learned...
research
∙
03/03/2021
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Recommender Systems have shown to be an effective way to alleviate the o...
research
∙
12/15/2020
FedeRank: User Controlled Feedback with Federated Recommender Systems
Recommender systems have shown to be a successful representative of how ...
research
∙
10/03/2020
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
Recommender systems (RSs) have attained exceptional performance in learn...
research
∙
10/02/2020
An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders
Visual-based recommender systems (VRSs) enhance recommendation performan...
research
∙
08/17/2020
How to Put Users in Control of their Data via Federated Pair-Wise Recommendation
Recommendation services are extensively adopted in several user-centered...
research
∙
07/17/2020
Prioritized Multi-Criteria Federated Learning
In Machine Learning scenarios, privacy is a crucial concern when models ...
research
∙
09/11/2019
How to make latent factors interpretable by feeding Factorization machines with knowledge graphs
Model-based approaches to recommendation can recommend items with a very...
research
∙
09/05/2019
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...
research
∙
08/20/2019
Towards Effective Device-Aware Federated Learning
With the wealth of information produced by social networks, smartphones,...
research
∙
08/19/2019
Recommender Systems Fairness Evaluation via Generalized Cross Entropy
Fairness in recommender systems has been considered with respect to sens...
research
∙
07/11/2018
The importance of being dissimilar in Recommendation
Similarity measures play a fundamental role in memory-based nearest neig...
research
∙
07/11/2018
