23 citations · 89 across the 24 of their papers we have counts for
26 papers
VIGIL: An Extensible System for Real-Time Detection and Mitigation of Cognitive Bias Triggers
Bo Kang, Sander Noels, Tijl De Bie
The rise of generative AI is posing increasing risks to online information integrity and civic discourse. Most concretely, such risks can materialise in the form of mis- and disinf…
Untangling Input Language from Reasoning Language: A Diagnostic Framework for Cross-Lingual Moral Alignment in LLMs
Nan Li, Bo Kang, Tijl De Bie
When LLMs judge moral dilemmas, do they reach different conclusions in different languages, and if so, why? Two factors could drive such differences: the language of the dilemma it…
Building Data-Driven Occupation Taxonomies: A Bottom-Up Multi-Stage Approach via Semantic Clustering and Multi-Agent Collaboration
Nan Li, Bo Kang, Tijl De Bie
Creating robust occupation taxonomies, vital for applications ranging from job recommendation to labor market intelligence, is challenging. Manual curation is slow, while existing…
Your Next State-of-the-Art Could Come from Another Domain: A Cross-Domain Analysis of Hierarchical Text Classification
Nan Li, Bo Kang, Tijl De Bie
Text classification with hierarchical labels is a prevalent and challenging task in natural language processing. Examples include assigning ICD codes to patient records, tagging pa…
Content-Agnostic Moderation for Stance-Neutral Recommendation
Nan Li, Bo Kang, Tijl De Bie
Personalized recommendation systems often drive users towards more extreme content, exacerbating opinion polarization. While (content-aware) moderation has been proposed to mitigat…
KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces
Alexander Rogiers, Maarten Buyl, Bo Kang +1
KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes ke…
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…
A challenge-based survey of e-recruitment recommendation systems
Yoosof Mashayekhi, Nan Li, Bo Kang +2
E-recruitment recommendation systems recommend jobs to job seekers and job seekers to recruiters. The recommendations are generated based on the suitability of the job seekers for…
SimHawNet: A Modified Hawkes Process for Temporal Network Simulation
Mathilde Perez, Raphaël Romero, Bo Kang +3
Temporal networks allow representing connections between objects while incorporating the temporal dimension. While static network models can capture unchanging topological regulari…
ExClus: Explainable Clustering on Low-dimensional Data Representations
Xander Vankwikelberge, Bo Kang, Edith Heiter +1
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret. We consider how to support u…
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?
Robin Vandaele, Bo Kang, Tijl De Bie +1
Distances between data points are widely used in machine learning applications. Yet, when corrupted by noise, these distances -- and thus the models based upon them -- may lose the…
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction
Xi Chen, Bo Kang, Jefrey Lijffijt +1
In today's networked society, many real-world problems can be formalized as predicting links in networks, such as Facebook friendship suggestions, e-commerce recommendations, and t…
FONDUE: A Framework for Node Disambiguation Using Network Embeddings
Ahmad Mel, Bo Kang, Jefrey Lijffijt +1
Real-world data often presents itself in the form of a network. Examples include social networks, citation networks, biological networks, and knowledge graphs. In their simplest fo…
ALPINE: Active Link Prediction using Network Embedding
Xi Chen, Bo Kang, Jefrey Lijffijt +1
Many real-world problems can be formalized as predicting links in a partially observed network. Examples include Facebook friendship suggestions, consumer-product recommendations,…
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach
Junning Deng, Bo Kang, Jefrey Lijffijt +1
The connectivity structure of graphs is typically related to the attributes of the nodes. In social networks for example, the probability of a friendship between two people depends…
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
Bo Kang, Darío García García, Jefrey Lijffijt +2
Dimensionality reduction and manifold learning methods such as t-Distributed Stochastic Neighbor Embedding (t-SNE) are routinely used to map high-dimensional data into a 2-dimensio…
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Bo Kang, Jefrey Lijffijt, Tijl De Bie
Networks are powerful data structures, but are challenging to work with for conventional machine learning methods. Network Embedding (NE) methods attempt to resolve this by learnin…
Conditional Network Embeddings
Bo Kang, Jefrey Lijffijt, Tijl De Bie
Network Embeddings (NEs) map the nodes of a given network into -dimensional Euclidean space . Ideally, this mapping is such that `similar' nodes are mapped onto ne…
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach
Kai Puolamäki, Emilia Oikarinen, Bo Kang +2
Visual exploration of high-dimensional real-valued datasets is a fundamental task in exploratory data analysis (EDA). Existing methods use predefined criteria to choose the represe…