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Found 12 papers, 9 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).
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.
Graph Neural Networks for Treatment Effect Prediction
Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings.
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.
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.
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?
