Mechanism Design for Mobile Geo-Location Advertising
Authors: Nicola Gatti, Marco Rocco, Sofia Ceppi, Enrico Gerding
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we experimentally evaluate the trade off between compute time and efficiency of our mechanisms. In our experiments we compare the run time and the quality of the solutions obtained using the above algorithms. |
| Researcher Affiliation | Collaboration | Nicola Gatti and Marco Rocco Politecnico di Milano Piazza Leonardo da Vinci 32 Milano, Italy {nicola.gatti, marco.rocco}@polimi.it Sofia Ceppi Microsoft Research 21 Station Road Cambridge, CB1 2FB, UK soceppi@microsoft.com Enrico H. Gerding University of Southampton University Road, Highfield Southampton, SO17 1BJ, UK eg@ecs.soton.ac.uk |
| Pseudocode | Yes | Algorithm 1 |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes a synthetic data generation process ('Instance generation' section) rather than using a pre-existing, publicly available dataset with concrete access information. It details how the experimental environment and data instances were created but does not provide a link or citation for a public dataset. |
| Dataset Splits | No | The paper describes the generation of experimental instances but does not provide specific details on training, validation, or test dataset splits, as it constructs data rather than partitioning a pre-existing dataset. |
| Hardware Specification | Yes | The experiments were conducted on a Unix computer with 2.33GHz CPU, 16Gb RAM, and kernel 2.6.32-45. |
| Software Dependencies | Yes | For our mathematical programming formulations we use AMPL as modeling language and CPLEX 11.0.1 to solve them. |
| Experiment Setup | Yes | We generate 50 instances for each of the following configurations: λ = 0.5 and N {10, 20, 30, 40, 50}, and λ = 0.8 and N {10, 20, 30}. In all instances |A| = 30. For multi path results: The figures show, for λ = 0.5, the average run time (left) and the AAR obtained with different values of m (right) as |Pv| varies, while the length of each path is uniformly drawn from {1, . . . , 20}. |