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..
Additional Multi-Touch Attribution for Online Advertising
Authors: Wendi Ji, Xiaoling Wang
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a large real-world advertising dataset illustrate that the our proposed method is superior to state-of-the-art techniques in conversion rate prediction and the credit allocation based on AMTA is reasonable. |
| Researcher Affiliation | Academia | Shanghai Key Laboratory of Trustworthy Computing, East China Normal University 3663 North Zhongshan Road, Shanghai, China EMAIL, EMAIL |
| Pseudocode | No | The paper provides mathematical equations and descriptions of methods but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a specific link or explicit statement) for the source code of the methodology described. |
| Open Datasets | No | The dataset is described as a 'real-world competition dataset provided by Miaozhen, a leading marketing technique company in China.' However, no direct URL, DOI, specific repository name, or citation to a public resource for accessing this dataset is provided. |
| Dataset Splits | Yes | the results are generated by 4-fold cross-validation over the users. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments, only mentioning the use of 'mini-batch stochastic gradient descent' for optimization. |
| Software Dependencies | No | The paper mentions implementing 'a gradient descent algorithm' and using 'mini-batch stochastic gradient descent', but it does not specify any software names with version numbers. |
| Experiment Setup | Yes | The optimization method is taken on the regularized negative log likelihood with respect to parameters of p, λ, and Λ: arg min Θ L (Θ) + μ; In our experiments, we have used mini-batch stochastic gradient descent to reduce the communication cost; We predict whether a user will convert in a specified upcoming period (30, 15 and 7 days); the results are generated by 4-fold cross-validation over the users; we sampled 1% negative users for model training. |