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].
Entire-Space Variational Information Exploitation for Post-Click Conversion Rate Prediction
Authors: Ke Fei, Xinyue Zhang, Jingjing Li
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive offline experiments on six large-scale datasets. EVI demonstrated a 2.25% average improvement compared to the state-of-the-art baselines. We conduct experiments using five large-scale public datasets and one private industrial dataset to validate the effectiveness of EVI. We compare EVI with 8 CVR estimation baselines. |
| Researcher Affiliation | Academia | Ke Fei, Xinyue Zhang, Jingjing Li* University of Electronic Science and Technology of China, Chengdu, China |
| Pseudocode | No | The paper describes the proposed method in Section 4 'Proposed Method' using prose and mathematical formulations, but no explicit pseudocode or algorithm blocks are provided. |
| Open Source Code | Yes | Code https://github.com/q1179897215/EVI Extended version https://github.com/q1179897215/EVI |
| Open Datasets | No | We conduct experiments using five large-scale public datasets and one private industrial dataset to validate the effectiveness of EVI. See Appendix A.1 for dataset information. (Appendix A.1 is not provided in the given text, and the main text does not provide specific URLs, DOIs, or citations with author/year for the public datasets mentioned.) |
| Dataset Splits | No | The paper mentions conducting experiments on 'test sets' in Section 5.3, but does not provide specific information about the training, validation, and test dataset splits (e.g., percentages, sample counts, or methodology) required for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU models, or cloud computing instances) used to conduct the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or frameworks) used in the implementation or experimentation. |
| Experiment Setup | No | The paper mentions 'See Appendix A.3 for implementation details' in Section 5, but Appendix A.3 is not provided in the given text. The main body of the paper does not contain specific experimental setup details such as hyperparameter values, optimizer settings, or training configurations. |