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..
STEM: Unleashing the Power of Embeddings for Multi-Task Recommendation
Authors: Liangcai Su, Junwei Pan, Ximei Wang, Xi Xiao, Shijie Quan, Xihua Chen, Jie Jiang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive evaluation on three public MTL recommendation datasets demonstrates that STEM-Net outperforms state-of-the-art models by a substantial margin. Our code is released at https://github.com/Liangcai Su/STEM. We conduct comprehensive experiments and ablation studies on three MTL recommendation datasets and provide compelling evidence of STEM-Net s effectiveness. |
| Researcher Affiliation | Collaboration | 1Shenzhen International Graduate School, Tsinghua University 2Tencent Inc. EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is released at https://github.com/Liangcai Su/STEM. |
| Open Datasets | Yes | Public Datasets. We choose three public datasets, namely Tik Tok, QK-Video (Yuan et al. 2022), and Kuai Rand1K (Yuan et al. 2022) for performance evaluation. |
| Dataset Splits | Yes | The statistics of the processed dataset is presented in Table 1. Tik Tok: #Samples 223.4M/24.8M/27.6M; QK-Video: #Samples 95.9M/12.0M/12.5M; Kuai Rand1K: #Samples 10.9M/0.39M/0.42M. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions implementing methods 'based on Pytorch' but does not provide specific version numbers for Pytorch or any other software dependencies. |
| Experiment Setup | Yes | We set the learning rate as {1e 3, 5e 4, 1e 4}, the batch size as 4096, and the l2 regularization factor of embedding as 1e 6. We set the dimension of the embedding to 16, and each expert/bottom is an MLP with hidden units of [512, 512, 512]. The towers and the gate networks of all methods are MLPs with hidden units of [128, 64]. The number of task-specific and shared experts is chosen from {1, 2, 4, 8}. Grid search is used to find optimal hyper-parameters for all methods. |