Ad Auctions and Cascade Model: GSP Inefficiency and Algorithms
Authors: Gabriele Farina, Nicola Gatti
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we provide exact, randomized, and approximate algorithms, showing that in real world settings (Yahoo! Webscope A3 dataset, 10 available slots) optimal allocations can be found in less than 1s with up to 1,000 ads, and can be approximated in less than 20ms even with more than 1,000 ads with an average accuracy greater than 99%. |
| Researcher Affiliation | Academia | Gabriele Farina and Nicola Gatti Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano Piazza Leonardo da Vinci, 32 I-20133, Milan, Italy gabriele2.farina@mail.polimi.it, nicola.gatti@polimi.it |
| Pseudocode | Yes | Algorithm 1 DOMINATED ADS; Algorithm 2 COLORED ADS; Algorithm 3 SORTED ADS |
| Open Source Code | No | The paper mentions an extended version of the paper for missing proofs and detailed experimental results, but does not provide concrete access to the source code for their methodology. |
| Open Datasets | Yes | The experimental setting is based on Yahoo! Webscope A3 dataset. |
| Dataset Splits | No | The paper mentions generating "20 instances" for each pair of (K, N) and describes how bids and quality are drawn from distributions, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | Yes | The main memory was a 16GB 1600MHz DDR3 RAM, while the processor was an Intel Core i7 4850HQ CPU. |
| Software Dependencies | Yes | We implemented our algorithms in the C++11 language and executed them on the OSX 10.10.3 operating system. ... We compiled the source with GNU g++ version 4.9.1. Parallelization was achieved using Open MP version 4.0. |
| Experiment Setup | Yes | The experimental setting is based on Yahoo! Webscope A3 dataset. Each bid is drawn from a truncated Gaussian distribution, where the mean and standard deviation are taken from the dataset, while quality is drawn from a beta distribution. The values of λs of the first 10 slots are {1.0, 0.71, 0.56, 0.53, 0.49, 0.47, 0.44, 0.44, 0.43, 0.43}. We considered two scenarios, one having K = 5 and one having K = 10. In both cases we let N {50, 60, . . . , 100} {200, 300, . . . , 1000}. For each pair (K, N), 20 instances were generated. |