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].
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. |