Edoardo D'Amico

According to our database, Edoardo D'Amico authored at least 9 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

RBoard: A Unified Platform for Reproducible and Reusable Recommender System Benchmarks.

Xinyang Shao, Edoardo D'Amico, Gábor Fodor, Tri Kurniawan Wijaya

CoRR, 2024

Rs4rs: Semantically Find Recent Publications from Top Recommendation System-Related Venues.

Tri Kurniawan Wijaya, Edoardo D'Amico, Gábor Fodor, Manuel V. Loureiro

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

2023

Scalable Deep Q-Learning for Session-Based Slate Recommendation.

Aayush Singha Roy, Edoardo D'Amico, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation.

Edoardo D'Amico, Aonghus Lawlor, Neil Hurley

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2023

Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems.

Aayush Singha Roy, Edoardo D'Amico, Aonghus Lawlor, Neil Hurley

Proceedings of the Thirty-Sixth International Florida Artificial Intelligence Research Society Conference, 2023

Item Graph Convolution Collaborative Filtering for Inductive Recommendations.

Edoardo D'Amico, Khalil Muhammad, Elias Z. Tragos, Barry Smyth, Neil Hurley, Aonghus Lawlor

Proceedings of the Advances in Information Retrieval, 2023

2022

Analyzing and improving stability of matrix factorization for recommender systems.

Edoardo D'Amico, Giovanni Gabbolini, Cesare Bernardis, Paolo Cremonesi

J. Intell. Inf. Syst., 2022

2021

On the instability of embeddings for recommender systems: the case of matrix factorization.

Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi

Proceedings of the SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, 2021

2019

Leveraging laziness, browsing-pattern aware stacked models for sequential accommodation learning to rank.

Edoardo D'Amico, Giovanni Gabbolini, Daniele Montesi, Matteo Moreschini, Federico Parroni, Federico Piccinini, Alberto Rossettini, Alessio Russo Introito, Cesare Bernardis, Maurizio Ferrari Dacrema

Proceedings of the Workshop on ACM Recommender Systems Challenge, 2019