Search Results for author:
Found 15 papers, 10 papers with code
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach
Inspired by the recent advances in graph representation learning, we propose to re-sketch the missing modalities problem as a problem of missing graph node features to apply the state-of-the-art feature propagation algorithm eventually.
Ducho 2.0: Towards a More Up-to-Date Unified Framework for the Extraction of Multimodal Features in Recommendation
In this work, we introduce Ducho 2. 0, the latest stable version of our framework.
Graph Neural Networks for Recommendation: Reproducibility, Graph Topology, and Node Representation
Graph neural networks (GNNs) have gained prominence in recommendation systems in recent years.
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.
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e. g., product images or descriptions) as items' side information to improve recommendation accuracy.
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.
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation
Motivated by the outlined aspects, we propose \framework, a unified framework for the extraction of multimodal features in recommendation.
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
EvalRS aims to bring together practitioners from industry and academia to foster a debate on rounded evaluation of recommender systems, with a focus on real-world impact across a multitude of deployment scenarios.
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.
Conversational Recommendation: Theoretical Model and Complexity Analysis
With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation.
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
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.
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.
