Truthful Auctions for Automated Bidding in Online Advertising
Authors: Yidan Xing, Zhilin Zhang, Zhenzhe Zheng, Chuan Yu, Jian Xu, Fan Wu, Guihai Chen
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate the performances of the proposed auction mechanism under various auto-bidding settings. The evaluation results demonstrate that the designed truthful auction can generally achieve more than 90% performance (in terms of revenue and social welfare) of the optimal baselines without the consideration of truthfulness. |
| Researcher Affiliation | Collaboration | Yidan Xing1 , Zhilin Zhang2 , Zhenzhe Zheng1, , Chuan Yu2 , Jian Xu2 , Fan Wu1 and Guihai Chen1 1Department of Computer Science and Engineering, Shanghai Jiao Tong University 2Alibaba Group |
| Pseudocode | Yes | Algorithm 1: A Family of Simple Truthful Auctions |
| Open Source Code | No | The paper does not contain an explicit statement or link to the open-source code for the methodology it describes. |
| Open Datasets | No | In this section, we conduct experiments with synthetic data to validate the performance of our proposed auctions. (...) Symmetric bidders Bidders are symmetric with vi,j U[1, 4], Bi U[40, 80] and Ri U[1, 3], where U[a, b] is the uniform distribution within the range [a, b]. |
| Dataset Splits | No | The paper uses synthetic data generated based on specified distributions for its experiments and does not describe fixed training, validation, and test dataset splits from a pre-existing dataset. Results are averaged over multiple runs with newly generated data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python, PyTorch, or specific solvers). |
| Experiment Setup | Yes | Rank Score Function We adopt rank scores in the form fi,j(R) = αi,j e βR, where αi,j is drawn from a rectified normal distribution N(µi, σ2 i ), and β, µi, σi are pre-set parameters. (...) Figure 3d reports a set of revenue and fairness performances with different β in the same setting as 400 items in Figure 3c. |