Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Lift-Based Bidding in Ad Selection
Authors: Jian Xu, Xuhui Shao, Jianjie Ma, Kuang-chih Lee, Hang Qi, Quan Lu
AAAI 2016 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |