no code implementations • 13 Nov 2023 • Linus W. Dietz, Pablo Sánchez, Alejandro Bellogín
The performance of recommendation algorithms is closely tied to key characteristics of the data sets they use, such as sparsity, popularity bias, and preference distributions.
1 code implementation • 1 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.
no code implementations • 2 Mar 2022 • Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Dietmar Jannach, Claudio Pomo
Moreover, we find that for none of the accuracy measurements any of the considered neural models led to the best performance.
no code implementations • 8 Oct 2021 • Alejandro Bellogín, Yashar Deldjoo
In this position paper, we discuss recent applications of simulation approaches for recommender systems tasks.
no code implementations • 2 Sep 2021 • Vito Walter Anelli, Alejandro Bellogín, Tommaso Di Noia, Francesco Maria Donini, Vincenzo Paparella, Claudio Pomo
Explainable Recommendation has attracted a lot of attention due to a renewed interest in explainable artificial intelligence.
Explainable artificial intelligence Explainable Recommendation
1 code implementation • 28 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.
no code implementations • 18 Jun 2021 • Pablo Sánchez, Alejandro Bellogín
Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems.
no code implementations • 26 Mar 2021 • Miriam Fernández, Alejandro Bellogín, Iván Cantador
Recommendation algorithms have been pointed out as one of the major culprits of misinformation spreading in the digital sphere.
1 code implementation • 3 Mar 2021 • Vito Walter Anelli, Alejandro Bellogín, Antonio Ferrara, Daniele Malitesta, Felice Antonio Merra, Claudio Pomo, Francesco Maria Donini, Tommaso Di Noia
Recommender Systems have shown to be an effective way to alleviate the over-choice problem and provide accurate and tailored recommendations.
no code implementations • 31 Jan 2021 • Alejandro Bellogín, Alan Said
In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works.
no code implementations • 3 Oct 2020 • Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra
However, while the single-step fast gradient sign method (FGSM) is the most explored perturbation strategy, multi-step (iterative) perturbation strategies, that demonstrated higher efficacy in the computer vision domain, have been highly under-researched in recommendation tasks.
1 code implementation • 26 Sep 2018 • Pablo Sánchez, Alejandro Bellogín
We perform an experimental comparison of several recommendation techniques in a temporal split under two conditions: single-domain (only information from the target city is considered) and cross- domain (information from many other cities is incorporated into the recommendation algorithm).