Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |