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 [1].
Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising
Authors: Ling Yan, Wu-Jun Li, Gui-Rong Xue, Dingyi Han
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on real-world data sets show that our CGL model can achieve state-of-the-art performance on webscale CTR prediction tasks. |
| Researcher Affiliation | Collaboration | Ling Yan EMAIL Shanghai Key Laboratory of Scalable Computing and Systems, Department of Computer Science and Engineering, Shanghai Jiao Tong University, China Wu-Jun Li EMAIL National Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, China Gui-Rong Xue EMAIL Alibaba Group, China Dingyi Han EMAIL Alibaba Group, China |
| Pseudocode | Yes | Algorithm 1 Alternate Learning for CGL |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We conduct our experiment on three real-world data sets connected from Taobao of Alibaba group. We build our data sets from the logs of the advertisements displayed in http://www.taobao.com, one of the most famous C2C e-commerce web sites in China. |
| Dataset Splits | Yes | We sample 20% of each training set for validation to specify the hyper-parameters of our CGL model and other baselines. |
| Hardware Specification | Yes | We have an MPI-cluster with hundreds of nodes, each of which is a 24-core server with 2.2GHz Intel(R) Xeon(R) E5-2430 processor and 96GB of RAM. |
| Software Dependencies | No | The paper mentions using MPI and L-BFGS algorithms but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | The k in CGL is fixed to 50 in our experiment unless otherwise stated. We vary the values of the hyper-parameter λ and draw the influence on the performance in Figure 3 (b). |