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..
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 | Venue PDF | 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 EMAIL |
| 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. |