23 citations · 89 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models
Nan Li, Bo Kang, Tijl De Bie
Automated occupation extraction and standardization from free-text job postings and resumes are crucial for applications like job recommendation and labor market policy formation.…
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…
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model
Nan Li, Bo Kang, Tijl De Bie
We present SkillGPT, a tool for skill extraction and standardization (SES) from free-style job descriptions and user profiles with an open-source Large Language Model (LLM) as back…
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…
Topologically Regularized Data Embeddings
Edith Heiter, Robin Vandaele, Tijl De Bie +2
Unsupervised representation learning methods are widely used for gaining insight into high-dimensional, unstructured, or structured data. In some cases, users may have prior topolo…