A variational neural architecture for skill-based team formation
Team formation is concerned with the identification of a group of experts who have a high
likelihood of effectively collaborating with each other to satisfy a collection of input skills …
likelihood of effectively collaborating with each other to satisfy a collection of input skills …
Efficient representation learning of subgraphs by subgraph-to-node translation
A subgraph is a data structure that can represent various real-world problems. We propose
Subgraph-To-Node (S2N) translation, which is a novel formulation to efficiently learn …
Subgraph-To-Node (S2N) translation, which is a novel formulation to efficiently learn …
Transfer learning with graph attention networks for team recommendation
In order to complete a common goal, team recommendation problems identify an efficient
group of experts who can collectively satisfy a set of required skills. A significant number of …
group of experts who can collectively satisfy a set of required skills. A significant number of …
A streaming approach to neural team formation training
Predicting future successful teams of experts who can effectively collaborate is challenging
due to the experts' temporality of skill sets, levels of expertise, and collaboration ties, which …
due to the experts' temporality of skill sets, levels of expertise, and collaboration ties, which …
Vector Representation Learning of Skills for Collaborative Team Recommendation: A Comparative Study
Neural team recommendation models have utilized graph representation learning to
achieve state-of-the-art performance in forming teams of experts whose success in …
achieve state-of-the-art performance in forming teams of experts whose success in …
Collaborative Team Recommendation for Skilled Users: Objectives, Techniques, and New Perspectives
Collaborative team recommendation involves selecting users with certain skills to form a
team who will, more likely than not, accomplish a complex task successfully. To automate …
team who will, more likely than not, accomplish a complex task successfully. To automate …
Generalizing Weisfeiler-Lehman Kernels to Subgraphs
Subgraph representation learning has been effective in solving various real-world problems.
However, current graph neural networks (GNNs) produce suboptimal results for subgraph …
However, current graph neural networks (GNNs) produce suboptimal results for subgraph …
: Mitigating Gender Bias in Neural Team Recommendation via Female-Advocate Loss Regularization
Neural team recommendation has brought state-of-the-art efficacy while enhancing
efficiency at forming teams of experts whose success in completing complex tasks is almost …
efficiency at forming teams of experts whose success in completing complex tasks is almost …
vivaFemme: Mitigating Gender Bias in Neural Team Recommendation via Female-Advocate Loss Regularization
R Moasses, D Rajaei, H Loghmani… - Advances in Bias and …, 2024 - Springer
Neural team recommendation has brought state-of-the-art efficacy while enhancing
efficiency at forming teams of experts whose success in completing complex tasks is almost …
efficiency at forming teams of experts whose success in completing complex tasks is almost …
Translating Subgraphs to Nodes Makes Simple GNNs Strong and Efficient for Subgraph Representation Learning
Subgraph representation learning has emerged as an important problem, but it is by default
approached with specialized graph neural networks on a large global graph. These models …
approached with specialized graph neural networks on a large global graph. These models …