UNBERT: User-News Matching BERT for News Recommendation
Authors: Qi Zhang, Jingjie Li, Qinglin Jia, Chuyuan Wang, Jieming Zhu, Zhaowei Wang, Xiuqiang He
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the Microsoft News Dataset (MIND) demonstrate that our approach consistently outperforms the state-of-the-art methods. |
| Researcher Affiliation | Industry | Huawei Noah’s Ark Lab {zhangqi193, lijingjie1, jiaqinglin2, wangchuyuan, jamie.zhu, wangzhaowei3, hexiuqiang1}@huawei.com |
| Pseudocode | No | The paper describes the model architecture and components in detail (e.g., Embedding Layer, Word-Level Module, News-Level Module, Click Predictor) and provides a diagram in Figure 3, but it does not include any structured pseudocode or explicitly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain an unambiguous statement that the authors are releasing their code for the work described, nor does it provide a direct link to a source-code repository for their implementation. |
| Open Datasets | Yes | We conduct experiments on a real-world news recommendation dataset MIND5 [Wu et al., 2020] collected from MSN News6 logs. 5https://msnews.github.io |
| Dataset Splits | Yes | The detailed statistics of the datasets are shown in Table 1. ... MIND-small MIND-large Train Dev Test Train Dev Test ... All the hyper-parameters are tuned on the validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'bert-base-uncased' as the pre-trained model and 'Adam' for optimization, but it does not specify version numbers for these or any other software components or libraries. |
| Experiment Setup | Yes | The batch size is set to 128, the learning rate is set to 2e-5, and 2 epochs are trained. |