Coupon Design in Advertising Systems
Authors: Weiran Shen, Pingzhong Tang, Xun Wang, Yadong Xu, Xiwang Yang5717-5725
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments are conducted to demonstrate the effectiveness of our algorithms based on both synthetic data and industrial data. |
| Researcher Affiliation | Collaboration | Weiran Shen,1 Pingzhong Tang, 2 Xun Wang, 2 Yadong Xu, 2 Xiwang Yang 3 1 Renmin University of China 2 Tsinghua University 3 Byte Dance |
| Pseudocode | Yes | Algorithm 1 Constructing m sub-VCG auctions; Algorithm 2 Algorithm for the no-feature case; Algorithm 3 Algorithm for the general case |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing the source code for their methodology. |
| Open Datasets | No | The paper states: "We use both synthetic data and industrial data to demonstrate the results of our experiments. As for synthetic data, we choose three different types of distribution to sample the value data... As for industrial data, it comes from one of the biggest short-form mobile video community in the world." However, it does not provide concrete access information (link, DOI, citation) for either the synthetic data generated or the industrial data, implying the latter is proprietary. |
| Dataset Splits | No | The paper states: "Then we run different algorithms on the training data and calculate ρa on the testing data. This procedure is repeated for 20 times..." It specifies a training and testing split (training data makes up about 70%) but does not mention a separate validation split for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU/CPU models, memory) used for running the experiments. It only mentions that Algorithm 3 is implemented using Gurobi 9.0. |
| Software Dependencies | Yes | Algorithm 3 is implemented using Gurobi 9.0 (Gurobi Optimization 2021). |
| Experiment Setup | Yes | We can see that Alg-3 always yields better performance than Alg-2 since it can utilize features. Algorithms may not converge within 20 iterations when λ is small, i.e., λ 0.1 in Alg-2 and λ = 0.01 in Alg-3. As for Alg-2, larger λ can achieve better performance, thus we choose λ = 0.5 in Alg-2 to guarantee convergence and achieve better performance. While for Alg-3, although larger λ can have higher revenue in some iterations, it is unstable during the training phase. Hence λ is set to 0.05 in Alg-3 to maintain robustness and obtain comparable performance. Besides, in the remaining experiments, we use ϵ = 0.001 and Kout = 20 in both algorithms. We choose c0 = 10 so that = 1. |