An Algorithm for Multi-Attribute Diverse Matching
Authors: Saba Ahmadi, Faez Ahmed, John P. Dickerson, Mark Fuge, Samir Khuller
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 7 Experimental Validation & Discussion To demonstrate the effectiveness of the proposed method, we apply it to a dataset of reviewer paper matching. |
| Researcher Affiliation | Academia | Saba Ahmadi1 , Faez Ahmed2 , John P. Dickerson1 , Mark Fuge3 and Samir Khuller4 1 Department of Computer Science, University of Maryland 2 Department of Mechanical Engineering, Northwestern University 3 Department of Mechanical Engineering, University of Maryland 4 Department of Computer Science, Northwestern University |
| Pseudocode | Yes | Algorithm 1: Find optimal diverse b-matching |
| Open Source Code | Yes | The source code is made available at https://github.com/ faezahmed/diverse matching. |
| Open Datasets | Yes | We use the multi-aspect review assignment evaluation dataset [Karimzadehgan and Zhai, 2009], a benchmark dataset from UIUC. |
| Dataset Splits | No | The paper describes the dataset and how reviewers were clustered and matched, but does not provide specific training, validation, or test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | Finally, we compare the timing performance of our algorithm with MIQP by changing the number of papers that need to be reviewed on a Dell XPS 13 laptop with i7 processor. |
| Software Dependencies | No | The paper mentions using 'Gurobi' for the MIQP solver but does not provide its specific version number or other software dependencies with version details. |
| Experiment Setup | Yes | For the reviewer assignment problem, where each reviewer has multiple features, we want to match each paper with reviewers who are not only from different expertise areas (clusters), but also belong to different genders. ... To set up the graph for our method, we first cluster the reviewers into 5 clusters based on their topic vectors using spectral clustering. ... We set the constraints such that each paper matches with exactly 4 reviewers, and no reviewer is allocated more than 1 paper. ... We set λ0 = λ1 = λ2 = 1 for our experiments. |