Distributed Ranking with Communications: Approximation Analysis and Applications
Authors: Hong Chen, Yingjie Wang, Yulong Wang, Feng Zheng7037-7045
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical and empirical assessments demonstrate the effectiveness of DLSRank-C under mild conditions.In this section, we evaluate DLSRank-C on some simulated and benchmark datasets to validate our theoretical findings.All experiments were implemented in MATLAB 2019b on an intel Core i7 with 16 GB memory. |
| Researcher Affiliation | Academia | 1College of Science, Huazhong Agricultural University, China 2College of Informatics, Huazhong Agricultural University, China 3Department of Computer Science and Engineering, Southern University of Science and Technology, China chenh@mail.hzau.edu.cn, yjaywang@126.com, wangyulong6251@gmail.com, zhengf@sustech.edu.cn |
| Pseudocode | Yes | Training flow: The training flow for DLSRank-C can be broken down into following five steps: Step 1 (Initialization): ... Step 2 (On each local machine): ... Step 3 (On global machine): ... Step 4 (On each local machine): ... Step 5 (On global machine): ... |
| Open Source Code | No | The paper does not provide any links to source code or explicitly state that the code for the described methodology is open-source or available. |
| Open Datasets | Yes | All data used here are freely available at: http://www.grouplens.org/taxonomy/term/14. The Movie Lens dataset consists of 25000095 anonymous ratings of 62423 movies made by 162541 Movie Lens users.We generate 10000 samples for training and 1000 samples for testing. |
| Dataset Splits | No | The paper mentions 10000 samples for training and 1000 for testing for simulated data, and an 8:2 ratio for training and testing on real-world data. However, it does not explicitly mention a separate 'validation' set or its split details. |
| Hardware Specification | Yes | All experiments were implemented in MATLAB 2019b on an intel Core i7 with 16 GB memory. |
| Software Dependencies | Yes | All experiments were implemented in MATLAB 2019b |
| Experiment Setup | Yes | The regularization parameter λ and bandwidth d are selected in the grids {10−2, 10−1, 1, 10, 100} and {1, 10, 102, 103}, respectively. |