192 citations · 434 across the 14 of their papers we have counts for
14 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…
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…
Automatic Generation of Natural Language Explanations
Felipe Costa, Sixun Ouyang, Peter Dolog +1
An important task for recommender system is to generate explanations according to a user's preferences. Most of the current methods for explainable recommendations use structured s…
Critical phenomena in heterogeneous k-core percolation
Davide Cellai, Aonghus Lawlor, Kenneth A. Dawson +1
-core percolation is a percolation model which gives a notion of network functionality and has many applications in network science. In analysing the resilience of a network und…
Tricritical point in heterogeneous k-core percolation
Davide Cellai, Aonghus Lawlor, Kenneth A. Dawson +1
k-core percolation is an extension of the concept of classical percolation and is particularly relevant to understand the resilience of complex networks under random damage. A new…