6 citations · 8 across the 3 of their papers we have counts for
3 papers
Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation
Edoardo D'Amico, Aonghus Lawlor, Neil Hurley
The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it h…
Item Graph Convolution Collaborative Filtering for Inductive Recommendations
Edoardo D'Amico, Khalil Muhammad, Elias Tragos +3
Graph Convolutional Networks (GCN) have been recently employed as core component in the construction of recommender system algorithms, interpreting user-item interactions as the ed…
On the instability of embeddings for recommender systems: the case of Matrix Factorization
Giovanni Gabbolini, Edoardo D'Amico, Cesare Bernardis +1
Most state-of-the-art top-N collaborative recommender systems work by learning embeddings to jointly represent users and items. Learned embeddings are considered to be effective to…