Lift-Based Bidding in Ad Selection
Authors: Jian Xu, Xuhui Shao, Jianjie Ma, Kuang-chih Lee, Hang Qi, Quan Lu
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We set up A/B test experiments on Yahoo’s Demand-Side Platform. ... The results shown in Table 2, 3, 4 and 5 backed up our claims and methods. |
| Researcher Affiliation | Industry | Jian Xu , Xuhui Shao, Jianjie Ma, Kuang-chih Lee, Hang Qi, Quan Lu Touch Pal Inc., 1172 Castro St, Mountain View, CA 94040 Yahoo Inc., 701 First Ave, Sunnyvale, CA 94089 jian.xu@cootek.cn, {xshao,jianma,kclee,hangqi,qlu}@yahoo-inc.com |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the methodology described. |
| Open Datasets | No | We set up A/B test experiments on Yahoo’s Demand-Side Platform. We selected five advertisers to participate in the test. |
| Dataset Splits | No | Our task is to train a generic AR prediction model ˆP to give AR estimations for both cases when an ad is shown or not shown. ... We set up A/B test experiments on Yahoo’s Demand-Side Platform. We first randomly split users into three equal-sized groups. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instances) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | Once the training samples are gathered, we train a Gradient-Boosting-Decision-Tree (GBDT) model to predict the rank order and then calibrate using isotonic regression to translate a GBDT score to an AR. Please note that we utilize our in-house GBDT tool with distributed training capability for modeling; however, other proper machine learning models can also be applied. |
| Experiment Setup | No | Our task is to train a generic AR prediction model... we train a Gradient-Boosting-Decision-Tree (GBDT) model to predict the rank order and then calibrate using isotonic regression to translate a GBDT score to an AR. |