192 citations · 434 across the 14 of their papers we have counts for
3 papers · 1 filter
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…
Improving Explainable Recommendations with Synthetic Reviews
Sixun Ouyang, Aonghus Lawlor, Felipe Costa +1
An important task for a recommender system to provide interpretable explanations for the user. This is important for the credibility of the system. Current interpretable recommende…