Latent Dirichlet Allocation for Internet Price War
Authors: Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong639-646
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Tests on simulated experiments and an open dataset for real data show that, by subsuming all available market information of the market maker s competitors, our model exhibits a significant improvement for understanding the market environment and finding the best response strategies in the Internet price war. |
| Researcher Affiliation | Collaboration | Shanghai Jiao Tong University , AI Department, Ant Financial Services Group , School of Electronics Engineering and Computer Science, Peking University lcc1992@sjtu.edu.cn, xyansjtu@163.com, xiaotie@pku.edu.cn, {yuan.qi, weichu.cw, le.song, junlong.qjl, yebai.hjs, junwu.xjw}@antfin.com |
| Pseudocode | Yes | Algorithm 1: The Process of the Internet Price War |
| Open Source Code | No | Not found. The paper does not provide a link to its source code or explicitly state that the code for its methodology is publicly available. |
| Open Datasets | Yes | Coupon Usage Data for O2O, referred to O2O Dataset in following description, is an open dataset from the Tianchi contest (Aliyun.com 2018). ... Aliyun.com. 2018. Coupon usage data for o2o. https://tianchi.alibabacloud.com/datalab/data Set.html?data Id=59. |
| Dataset Splits | No | Not found. The paper states 'original dataset is randomly split into training dataset and testing dataset with ratio 9:1', but does not mention a separate validation split. |
| Hardware Specification | No | Not found. The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | Not found. The paper mentions 'implemented by sklearn', but does not provide specific version numbers for software libraries or environments. |
| Experiment Setup | Yes | The network of adopted DQN has 3 layers, the sizes of which are Ninput, 512, 5. The reward function is R(st j, bt j) = ct j 0.5 cost(bt j) and its decay rate is 0.9. The learning rate is 0.01 and memory size is 200000. |