Online Pricing for Revenue Maximization with Unknown Time Discounting Valuations
Authors: Weichao Mao, Zhenzhe Zheng, Fan Wu, Guihai Chen
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical results show that our design achieves good performance in terms of revenue maximization on a real-world bidding dataset. |
| Researcher Affiliation | Academia | 1 Shanghai Key Laboratory of Scalable Computing and Systems, Shanghai Jiao Tong University, China 2 Department of Computer Science and Technology, Nanjing University, China |
| Pseudocode | Yes | Algorithm 1: Descend Upon Rejection, Algorithm 2: Biased-UCB, Algorithm 3: Update Weight |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use the real-world bidding feedback log [Zhang et al., 2014] from the i Pin You company as our dataset. |
| Dataset Splits | No | The paper mentions selecting 2000 bids for each run but does not specify explicit training, validation, or test dataset splits with percentages, counts, or references to predefined splits necessary for reproduction of data partitioning in a typical machine learning sense. The online nature of the problem means data is processed sequentially rather than split statically for training/validation/testing of a learned model. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory, or specific computing environments) used for running the experiments. |
| Software Dependencies | No | The paper does not provide any specific software names with version numbers (e.g., libraries, frameworks, or programming languages with their versions) that are required to replicate the experiment. |
| Experiment Setup | Yes | We set price discretization level β = 0.2, exploration-exploitation control parameter τ = 0.5 for Rexp3 and c = 400.0 for other algorithms, exponential discount factor α = 1.003, attenuation factor γ = 0.9 for Biased-UCB and D-UCB, and batch size = 400 for Rexp3. |