Curriculum Multi-Level Learning for Imbalanced Live-Stream Recommendation
Authors: Shuodian Yu, Junqi Jin, Li Ma, Xiaofeng Gao, Xiaopeng Wu, Haiyang Xu, Jian Xu
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on a live-stream production dataset demonstrate the superiority of the proposed framework. |
| Researcher Affiliation | Collaboration | 1Mo E Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China 2Alibaba Group, Beijing, China |
| Pseudocode | Yes | Algorithm 1: Training Algorithm for CMLIR |
| Open Source Code | No | The paper does not provide a statement about open-sourcing the code for the described methodology or a link to a code repository. |
| Open Datasets | No | To exploit the effectiveness of CMLIR, we conduct our experiments on a dataset collected from Taobao Live, a popular live-stream e-commerce platform with hundreds of millions of users and tens of thousands of streamers. |
| Dataset Splits | Yes | We take the first 5 days of data as the training set, and the last day is used for validation and testing. |
| Hardware Specification | Yes | All the above models are trained with Tesla T4 GPU and implemented with Tensorflow 1.14. |
| Software Dependencies | Yes | implemented with Tensorflow 1.14. |
| Experiment Setup | Yes | The loss functions are Binary Cross Entropy following backbone model. The batch size is set to 512 and the learning rate is 0.001. We use Re LU [Nair and Hinton, 2010] as the activation function for all methods. The DNN dimension of experts in MMo E and PLE are set as [128, 64] for all methods. For CMLIR, the iteration number is set as 25, the initial step size is 1.0, the adaptor coefficient η is 1.0, and the step number k in specific learning is set as 4. |