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Found 27 papers, 11 papers with code
Scalable Cloud-Native Pipeline for Efficient 3D Model Reconstruction from Monocular Smartphone Images
In recent years, 3D models have gained popularity in various fields, including entertainment, manufacturing, and simulation.
Enhancing Sequential Music Recommendation with Personalized Popularity Awareness
In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption.
A Novel Evaluation Perspective on GNNs-based Recommender Systems through the Topology of the User-Item Graph
Recently, graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation.
Evaluating ChatGPT as a Recommender System: A Rigorous Approach
However, although various methods have been proposed to integrate ChatGPT's capabilities into RSs, current research struggles to comprehensively evaluate such models while considering the peculiarities of generative 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.
Post-hoc Selection of Pareto-Optimal Solutions in Search and Recommendation
To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier.
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.
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.
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art
Moreover, we find that for none of the accuracy measurements any of the considered neural models led to the best performance.
Adherence and Constancy in LIME-RS Explanations for Recommendation
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence.
Explainable artificial intelligence
Explainable Recommendation
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
However, a key overlooked aspect has been the beyond-accuracy performance of APR, i. e., novelty, coverage, and amplification of popularity bias, considering that recent results suggest that BPR, the building block of APR, is sensitive to the intensification of biases and reduction of recommendation novelty.
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.
Reenvisioning Collaborative Filtering vs Matrix Factorization
We replicate experiments from three papers that compare Neural Collaborative Filtering (NCF) and Matrix Factorization (MF), to extend the analysis to other evaluation dimensions.
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.
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks.
An Empirical Study of DNNs Robustification Inefficacy in Protecting Visual Recommenders
However, since adversarial training techniques have proven to successfully robustify DNNs in preserving classification accuracy, to the best of our knowledge, two important questions have not been investigated yet: 1) How well can these defensive mechanisms protect the VRSs performance?
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.
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.
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.
Recommender Systems Fairness Evaluation via Generalized Cross Entropy
We present a probabilistic framework based on generalized cross entropy to evaluate fairness of recommender systems under this perspective, where we show that the proposed framework is flexible and explanatory by allowing to incorporate domain knowledge (through an ideal fair distribution) that can help to understand which item or user aspects a recommendation algorithm is over- or under-representing.
The importance of being dissimilar in Recommendation
Similarity measures play a fundamental role in memory-based nearest neighbors approaches.
Local Popularity and Time in top-N Recommendation
Items popularity is a strong signal in recommendation algorithms.
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.
