Search Results for author:
Found 20 papers, 9 papers with code
FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources
In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness.
LLM4Jobs: Unsupervised occupation extraction and standardization leveraging Large Language Models
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
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.
SkillGPT: a RESTful API service for skill extraction and standardization using a Large Language Model
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.
Revised Conditional t-SNE: Looking Beyond the Nearest Neighbors
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.
A challenge-based survey of e-recruitment recommendation systems
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.
Graph-Survival: A Survival Analysis Framework for Machine Learning on Temporal Networks
Continuous time temporal networks are attracting increasing attention due their omnipresence in real-world datasets and they manifold applications.
ExClus: Explainable Clustering on Low-dimensional Data Representations
Dimensionality reduction and clustering techniques are frequently used to analyze complex data sets, but their results are often not easy to interpret.
Topologically Regularized Data Embeddings
For tasks for which prior expert topological knowledge is available, incorporating this into the learned representation may lead to higher quality embeddings.
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?
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.
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction
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?
FONDUE: A Framework for Node Disambiguation Using Network Embeddings
Real-world data often presents itself in the form of a network.
ALPINE: Active Link Prediction using Network Embedding
Often, the link status of a node pair can be queried, which can be used as additional information by the link prediction algorithm.
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach
The connectivity structure of graphs is typically related to the attributes of the nodes.
Conditional t-SNE: Complementary t-SNE embeddings through factoring out prior information
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.
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions
Networks are powerful data structures, but are challenging to work with for conventional machine learning methods.
Conditional Network Embeddings
Network Embeddings (NEs) map the nodes of a given network into $d$-dimensional Euclidean space $\mathbb{R}^d$.
Interactive Visual Data Exploration with Subjective Feedback: An Information-Theoretic Approach
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.
Subjectively Interesting Subgroup Discovery on Real-valued Targets
The subgroup descriptions are in terms of a succinct set of arbitrarily-typed other attributes.
Informative Data Projections: A Framework and Two Examples
Methods for Projection Pursuit aim to facilitate the visual exploration of high-dimensional data by identifying interesting low-dimensional projections.
