102 citations · 237 across the 11 of their papers we have counts for
11 papers
On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis
Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo +1
Multimodal-aware recommender systems (MRSs) exploit multimodal content (e.g., product images or descriptions) as items' side information to improve recommendation accuracy. While m…
A Topology-aware Analysis of Graph Collaborative Filtering
Daniele Malitesta, Claudio Pomo, Vito Walter Anelli +3
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…
Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis
Vito Walter Anelli, Daniele Malitesta, Claudio Pomo +3
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. How…
Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation
Daniele Malitesta, Giuseppe Gassi, Claudio Pomo +1
In multimodal-aware recommendation, the extraction of meaningful multimodal features is at the basis of high-quality recommendations. Generally, each recommendation framework imple…
EvalRS 2023. Well-Rounded Recommender Systems For Real-World Deployments
Federico Bianchi, Patrick John Chia, Ciro Greco +5
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 m…
Top-N Recommendation Algorithms: A Quest for the State-of-the-Art
Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia +2
Research on recommender systems algorithms, like other areas of applied machine learning, is largely dominated by efforts to improve the state-of-the-art, typically in terms of acc…
Conversational Recommendation: Theoretical Model and Complexity Analysis
Tommaso Di Noia, Francesco Donini, Dietmar Jannach +2
Recommender systems are software applications that help users find items of interest in situations of information overload in a personalized way, using knowledge about the needs an…
Adherence and Constancy in LIME-RS Explanations for Recommendation
Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia +3
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence. In particular, post-hoc approaches have proved to be t…
Reenvisioning Collaborative Filtering vs Matrix Factorization
Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia +1
Collaborative filtering models based on matrix factorization and learned similarities using Artificial Neural Networks (ANNs) have gained significant attention in recent years. Thi…
Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation
Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara +5
Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of prop…
On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them
Vito Walter Anelli, Tommaso Di Noia, Eugenio Di Sciascio +2
Hyper-parameters tuning is a crucial task to make a model perform at its best. However, despite the well-established methodologies, some aspects of the tuning remain unexplored. As…