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Edoardo D'Amico
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2020 – today
- 2023
- [c6]Edoardo D'Amico
, Khalil Muhammad
, Elias Z. Tragos
, Barry Smyth
, Neil Hurley
, Aonghus Lawlor
:
Item Graph Convolution Collaborative Filtering for Inductive Recommendations. ECIR (1) 2023: 249-263 - [c5]Aayush Singha Roy
, Edoardo D'Amico
, Aonghus Lawlor
, Neil Hurley
:
Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems. FLAIRS 2023 - [c4]Edoardo D'Amico
, Aonghus Lawlor
, Neil Hurley
:
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation. PAKDD (3) 2023: 310-321 - [c3]Aayush Singha Roy
, Edoardo D'Amico
, Elias Z. Tragos
, Aonghus Lawlor
, Neil Hurley
:
Scalable Deep Q-Learning for Session-Based Slate Recommendation. RecSys 2023: 877-882 - [i3]Edoardo D'Amico, Khalil Muhammad, Elias Z. Tragos, Barry Smyth, Neil Hurley, Aonghus Lawlor:
Item Graph Convolution Collaborative Filtering for Inductive Recommendations. CoRR abs/2303.15946 (2023) - [i2]Edoardo D'Amico, Aonghus Lawlor, Neil Hurley:
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation. CoRR abs/2305.18374 (2023) - 2022
- [j1]Edoardo D'Amico, Giovanni Gabbolini, Cesare Bernardis
, Paolo Cremonesi:
Analyzing and improving stability of matrix factorization for recommender systems. J. Intell. Inf. Syst. 58(2): 255-285 (2022) - 2021
- [c2]Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi:
On the instability of embeddings for recommender systems: the case of matrix factorization. SAC 2021: 1363-1370 - [i1]Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis, Paolo Cremonesi:
On the instability of embeddings for recommender systems: the case of Matrix Factorization. CoRR abs/2104.05796 (2021)
2010 – 2019
- 2019
- [c1]Edoardo D'Amico, Giovanni Gabbolini, Daniele Montesi, Matteo Moreschini, Federico Parroni, Federico Piccinini, Alberto Rossettini, Alessio Russo Introito, Cesare Bernardis, Maurizio Ferrari Dacrema
:
Leveraging laziness, browsing-pattern aware stacked models for sequential accommodation learning to rank. RecSys Challenge 2019: 7:1-7:5
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