Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Pomo, Claudio"'
Autor:
Mancino, Alberto Carlo Maria, Bufi, Salvatore, Di Fazio, Angela, Malitesta, Daniele, Pomo, Claudio, Ferrara, Antonio, Di Noia, Tommaso
Thanks to the great interest posed by researchers and companies, recommendation systems became a cornerstone of machine learning applications. However, concerns have arisen recently about the need for reproducibility, making it challenging to identif
Externí odkaz:
http://arxiv.org/abs/2410.22972
Autor:
Kruse, Johannes, Lindskow, Kasper, Kalloori, Saikishore, Polignano, Marco, Pomo, Claudio, Srivastava, Abhishek, Uppal, Anshuk, Andersen, Michael Riis, Frellsen, Jes
Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing.
Externí odkaz:
http://arxiv.org/abs/2410.03432
Autor:
Kruse, Johannes, Lindskow, Kasper, Kalloori, Saikishore, Polignano, Marco, Pomo, Claudio, Srivastava, Abhishek, Uppal, Anshuk, Andersen, Michael Riis, Frellsen, Jes
The RecSys Challenge 2024 aims to advance news recommendation by addressing both the technical and normative challenges inherent in designing effective and responsible recommender systems for news publishing. This paper describes the challenge, inclu
Externí odkaz:
http://arxiv.org/abs/2409.20483
Autor:
Attimonelli, Matteo, Danese, Danilo, Di Fazio, Angela, Malitesta, Daniele, Pomo, Claudio, Di Noia, Tommaso
In specific domains like fashion, music, and movie recommendation, the multi-faceted features characterizing products and services may influence each customer on online selling platforms differently, paving the way to novel multimodal recommendation
Externí odkaz:
http://arxiv.org/abs/2409.15857
Autor:
Malitesta, Daniele, Rossi, Emanuele, Pomo, Claudio, Di Noia, Tommaso, Malliaros, Fragkiskos D.
Generally, items with missing modalities are dropped in multimodal recommendation. However, with this work, we question this procedure, highlighting that it would further damage the pipeline of any multimodal recommender system. First, we show that t
Externí odkaz:
http://arxiv.org/abs/2408.11767
Autor:
Malitesta, Daniele, Pomo, Claudio, Anelli, Vito Walter, Mancino, Alberto Carlo Maria, Di Noia, Tommaso, Di Sciascio, Eugenio
Recently, graph neural networks (GNNs)-based recommender systems have encountered great success in recommendation. As the number of GNNs approaches rises, some works have started questioning the theoretical and empirical reasons behind their superior
Externí odkaz:
http://arxiv.org/abs/2408.11762
The increasing demand for online fashion retail has boosted research in fashion compatibility modeling and item retrieval, focusing on matching user queries (textual descriptions or reference images) with compatible fashion items. A key challenge is
Externí odkaz:
http://arxiv.org/abs/2408.09847
Autor:
Malitesta, Daniele, Rossi, Emanuele, Pomo, Claudio, Malliaros, Fragkiskos D., Di Noia, Tommaso
Multimodal recommender systems work by augmenting the representation of the products in the catalogue through multimodal features extracted from images, textual descriptions, or audio tracks characterising such products. Nevertheless, in real-world a
Externí odkaz:
http://arxiv.org/abs/2403.19841
Autor:
Attimonelli, Matteo, Danese, Danilo, Malitesta, Daniele, Pomo, Claudio, Gassi, Giuseppe, Di Noia, Tommaso
In this work, we introduce Ducho 2.0, the latest stable version of our framework. Differently from Ducho, Ducho 2.0 offers a more personalized user experience with the definition and import of custom extraction models fine-tuned on specific tasks and
Externí odkaz:
http://arxiv.org/abs/2403.04503
Graph neural networks (GNNs) have gained prominence in recommendation systems in recent years. By representing the user-item matrix as a bipartite and undirected graph, GNNs have demonstrated their potential to capture short- and long-distance user-i
Externí odkaz:
http://arxiv.org/abs/2310.11270