Optimal Auction Design with User Coupons in Advertising Systems
Authors: Xiaodong Liu, Zhikang Fan, Yiming Ding, Yuan Guo, Lihua Zhang, Changcheng Li, Dongying Kong, Han Li, Weiran Shen
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on both synthetic data and industrial data. |
| Researcher Affiliation | Collaboration | 1Renmin University of China 2Kuaishou Technology Co., Ltd |
| Pseudocode | No | The paper describes the experimental procedures in text, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a specific repository link, an explicit code release statement, or mention code in supplementary materials for the methodology described. |
| Open Datasets | No | The paper uses synthetic data which is generated, and industrial data from "Kuaishou, a major short-form video and live-streaming platform." No concrete access information (link, DOI, repository name, formal citation) for a publicly available dataset is provided. |
| Dataset Splits | Yes | We use 80,000 auctions to select the best coupon strategies for the second and third baseline algorithms. We use the other 20,000 auctions to verify the experimental results between our method and the baseline methods. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions training a CVR model but does not provide specific software dependency details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We generate a data set using a method similar to the one in [Ni et al., 2023]. We consider a pre-defined coupon set that contains only several possible coupon values, and we generate the corresponding CTRs and CVRs. For each advertiser i, we enumerate all coupons and compute each advertiser s best coupon using our mechanism. We train a CVR model using the collected data...Then we discrete the coupon space for each advertiser, compute the φi(ci, vi) for each possible coupon ci, and choose the maximum one as the optimal coupon. |