1 code implementation • 8 Nov 2023 • Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie
In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness.
1 code implementation • 18 Sep 2023 • 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.
1 code implementation • 18 Aug 2023 • Yoosof Mashayekhi, Bo Kang, Jefrey Lijffijt, Tijl De Bie
Recommenders are increasingly used in domains where items have limited availability, such as the job market, where congestion is especially problematic: Recommending a vacancy -- for which typically only one person will be hired -- to a large number of job seekers may lead to frustration for job seekers, as they may be applying for jobs where they are not hired.
1 code implementation • 17 Apr 2023 • 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 backbone.
1 code implementation • 7 Feb 2023 • Edith Heiter, Bo Kang, Ruth Seurinck, Jefrey Lijffijt
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 label information.
no code implementations • 12 Sep 2022 • Yoosof Mashayekhi, Nan Li, Bo Kang, Jefrey Lijffijt, Tijl De Bie
The recommendations are generated based on the suitability of the job seekers for the positions as well as the job seekers' and the recruiters' preferences.
no code implementations • 14 Mar 2022 • Raphaël Romero, Bo Kang, Tijl De Bie
Continuous time temporal networks are attracting increasing attention due their omnipresence in real-world datasets and they manifold applications.
no code implementations • 4 Nov 2021 • Xander Vankwikelberge, Bo Kang, Edith Heiter, Jefrey Lijffijt
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret.
1 code implementation • ICLR 2022 • Robin Vandaele, Bo Kang, Jefrey Lijffijt, Tijl De Bie, Yvan Saeys
For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings.
1 code implementation • 22 Sep 2021 • Robin Vandaele, Bo Kang, Tijl De Bie, Yvan Saeys
Previously, it has been argued that neighborhood queries become meaningless and unstable when distance concentration occurs, which means that there is a poor relative discrimination between the furthest and closest neighbors in the data.
no code implementations • 5 Jul 2021 • Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie
However, these methods lack transparency when compared to simpler baselines, and as a result their robustness against adversarial attacks is a possible point of concern: could one or a few small adversarial modifications to the network have a large impact on the link prediction performance when using a network embedding model?
no code implementations • 24 Feb 2020 • Ahmad Mel, Bo Kang, Jefrey Lijffijt, Tijl De Bie
Real-world data often presents itself in the form of a network.
no code implementations • 4 Feb 2020 • Xi Chen, Bo Kang, Jefrey Lijffijt, Tijl De Bie
Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm.
1 code implementation • 10 Jan 2020 • Junning Deng, Bo Kang, Jefrey Lijffijt, Tijl De Bie
The connectivity structure of graphs is typically related to the attributes of the nodes.
no code implementations • 24 May 2019 • Bo Kang, Darío García García, Jefrey Lijffijt, Raúl Santos-Rodríguez, Tijl De Bie
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-dimensional space to visualize and explore the data.
no code implementations • 22 Apr 2019 • Bo Kang, Jefrey Lijffijt, Tijl De Bie
Networks are powerful data structures, but are challenging to work with for conventional machine learning methods.
no code implementations • ICLR 2019 • Bo Kang, Jefrey Lijffijt, Tijl De Bie
Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$.
1 code implementation • 23 Oct 2017 • Kai Puolamäki, Emilia Oikarinen, Bo Kang, Jefrey Lijffijt, Tijl De Bie
We conclude that the information theoretic approach to exploratory data analysis where patterns observed by a user are formalized as constraints provides a principled, intuitive, and efficient basis for constructing an EDA system.
no code implementations • 12 Oct 2017 • Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie
The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.
no code implementations • 27 Nov 2015 • Tijl De Bie, Jefrey Lijffijt, Raul Santos-Rodriguez, Bo Kang
Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections.