User Retention: A Causal Approach with Triple Task Modeling
Authors: Yang Zhang, Dong Wang, Qiang Li, Yue Shen, Ziqi Liu, Xiaodong Zeng, Zhiqiang Zhang, Jinjie Gu, Derek F. Wong
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on both offline and online environments from different scenarios demonstrate the superiority of UR-IPW over previous methods. We conduct extensive experiments on both offline and online environments. |
| Researcher Affiliation | Collaboration | Yang Zhang1,2 , Dong Wang1 , Qiang Li1 , Yue Shen1 , Ziqi Liu1 , Xiaodong Zeng1 , Zhiqiang Zhang1 , Jinjie Gu1 and Derek F. Wong3 1Ant Group, Hangzhou, China 2Beihang University, Beijing, China 3University of Macau, Macau, China |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | No | Our two production datasets are collected from Alipay s recommender system. The release of related datasets requires strict approval, we are going through the relevant approval procedure; The data set is only used for academic research, it does not represent any real business situation. |
| Dataset Splits | No | For each dataset, we split the first 4 days in the time sequence to be training set while the rest to be test set. The paper specifies training and test sets but does not explicitly mention a separate validation set split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU or CPU models. |
| Software Dependencies | Yes | All the deep neural network-based models are implemented in Tensor Flow v1.13 using Adam optimizer. |
| Experiment Setup | Yes | The learning rate is set as 0.0005 and the mini-batch size is set as 1024. Cross-entropy loss function is used for each prediction task in all models. There is 5 layers in the MLP, where the dimension of each layer is set as 512 256 128 32 2. |