Search Results for author: Claudio Pomo

Found 13 papers, 9 papers with code

Formalizing Multimedia Recommendation through Multimodal Deep Learning

1 code implementation11 Sep 2023 Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Felice Antonio Merra, Tommaso Di Noia, Eugenio Di Sciascio

Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains.

Benchmarking Multimodal Deep Learning +1

On Popularity Bias of Multimodal-aware Recommender Systems: a Modalities-driven Analysis

1 code implementation24 Aug 2023 Daniele Malitesta, Giandomenico Cornacchia, Claudio Pomo, Tommaso Di Noia

Multimodal-aware recommender systems (MRSs) exploit multimodal content (e. g., product images or descriptions) as items' side information to improve recommendation accuracy.

Recommendation Systems

A Topology-aware Analysis of Graph Collaborative Filtering

1 code implementation21 Aug 2023 Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo Maria Mancino, Eugenio Di Sciascio, Tommaso Di Noia

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).

Collaborative Filtering Graph Sampling +1

Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis

1 code implementation1 Aug 2023 Vito Walter Anelli, Daniele Malitesta, Claudio Pomo, Alejandro Bellogín, Tommaso Di Noia, Eugenio Di Sciascio

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.

Collaborative Filtering Recommendation Systems

Ducho: A Unified Framework for the Extraction of Multimodal Features in Recommendation

1 code implementation29 Jun 2023 Daniele Malitesta, Giuseppe Gassi, Claudio Pomo, Tommaso Di Noia

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

1 code implementation14 Apr 2023 Federico Bianchi, Patrick John Chia, Ciro Greco, Claudio Pomo, Gabriel Moreira, Davide Eynard, Fahd Husain, Jacopo Tagliabue

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.

Fairness Informativeness +1

Conversational Recommendation: Theoretical Model and Complexity Analysis

no code implementations10 Nov 2021 Tommaso Di Noia, Francesco Donini, Dietmar Jannach, Fedelucio Narducci, Claudio Pomo

With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation.

Recommendation Systems

Reenvisioning Collaborative Filtering vs Matrix Factorization

1 code implementation28 Jul 2021 Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Claudio Pomo

We replicate experiments from three papers that compare Neural Collaborative Filtering (NCF) and Matrix Factorization (MF), to extend the analysis to other evaluation dimensions.

Collaborative Filtering

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