Improved Online Learning Algorithms for CTR Prediction in Ad Auctions
Authors: Zhe Feng, Christopher Liaw, Zixin Zhou
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we investigate the online learning problem of revenue maximization in ad auctions, where the seller needs to learn the click-through rates (CTRs) of each ad candidate and charge the price of the winner through a pay-per-click manner. We focus on two models of the advertisers strategic behaviors. First, we assume that the advertiser is completely myopic; i.e. in each round, they aim to maximize their utility only for the current round. In this setting, we develop an online mechanism based on upper-confidence bounds that achieves a tight O( T) regret in the worstcase and negative regret when the values are static across all the auctions and there is a gap between the highest expected value (i.e. value multiplied by their CTR) and second highest expected value ad. Next, we assume that the advertiser is nonmyopic and cares about their long term utility. In this setting, we provide an algorithm to achieve negative regret for the static valuation setting (with a positive gap), which is in sharp contrast with the prior work that shows O(T 2/3) regret when the valuation is generated by adversary. |
| Researcher Affiliation | Collaboration | 1Google, Mountain View, USA 2Stanford University, Stanford, USA. |
| Pseudocode | Yes | Algorithm 1 UCB-style algorithm for online pay-per-click auctions |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper does not describe experiments using publicly available datasets; it focuses on theoretical analysis of valuation generation settings. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments that would involve training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |