2 citations · 3 across the 5 of their papers we have counts for
5 papers
Pattern or Artifact? Interactively Exploring Embedding Quality with TRACE
Edith Heiter, Liesbet Martens, Ruth Seurinck +4
This paper presents TRACE, a tool to analyze the quality of 2D embeddings generated through dimensionality reduction techniques. Dimensionality reduction methods often prioritize p…
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
Nan Li, Bo Kang, Jefrey Lijffijt +1
In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness. We i…
ReCon: Reducing Congestion in Job Recommendation using Optimal Transport
Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt +1
Recommender systems may suffer from congestion, meaning that there is an unequal distribution of the items in how often they are recommended. Some items may be recommended much mor…
Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors
Edith Heiter, Bo Kang, Ruth Seurinck +1
Conditional t-SNE (ct-SNE) is a recent extension to t-SNE that allows removal of known cluster information from the embedding, to obtain a visualization revealing structure beyond…
A Systematic Evaluation of Node Embedding Robustness
Alexandru Mara, Jefrey Lijffijt, Stephan Günnemann +1
Node embedding methods map network nodes to low dimensional vectors that can be subsequently used in a variety of downstream prediction tasks. The popularity of these methods has g…