Score Aggregation via Spectral Method
Authors: Mingyu Xiao, Yuqing Wang
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we give computational results on some real word data as examples. |
| Researcher Affiliation | Academia | Mingyu Xiao and Yuqing Wang School of Computer Science and Engineering, University of Electronic Science and Technology of China, China myxiao@gmail.com, yyqqwang@126.com |
| Pseudocode | Yes | Algorithm SPECTRAL Input: A score matrix X Output: A vector corresponding to the aggregate scores of candidates 1. Compute the judgement matrix Z = XT X; 2. Conduct eigenvalue decomposition to Z, let λmax be the largest eigenvalue and e be the eigenvector corresponding to it; 3. Return v = X e. |
| Open Source Code | No | The paper does not provide any links or explicit statements regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Our data is from Group Lens Research (https://grouplens.org/datasets/movielens/), which collects the rating data for movies from the web site Movie Lens (http://movielens.org) [Harper and Konstan, 2016]. Our data is from the web site of CWTS Leiden Ranking (http://www.leidenranking.com/). |
| Dataset Splits | No | The paper describes using specific datasets but does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the computational examples and applications but does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings. |