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..
Semi-supervised User Profiling with Heterogeneous Graph Attention Networks
Authors: Weijian Chen, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo, Dawei Yin, Yongdong Zhang
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profiling. |
| Researcher Affiliation | Collaboration | 1 University of Science and Technology of China, Hefei, China 2 JD.com, China 3 Shandong University, China |
| Pseudocode | No | The paper describes various operations but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is open-source or publicly available. |
| Open Datasets | No | To evaluate our proposed method in user profiling, we collect a large scale real-world dataset from JD.com , one of the most popular e-commerce portals in China. |
| Dataset Splits | Yes | In the experiment, we randomly split labeled users into training set, validation set and test set with the ratio 75:12.5:12.5 following previous works [Qiu et al., 2018]. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions using Fast Text and Adam optimizer but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In the Mini Heterogeneous Graph Sampling procedure, the number of neighborhood samples is set as follows: k = 2, Lu1 = 10, Lu2 = 4 for User-User mini graph, Li = 10 for Item-User mini graph, Lt = 10 for Attribute-Item mini graph. [...] The learning rate, dropout rate, mini-batch size, are set to 0.005, 0.6, 64 for gender prediction and 0.1, 0.2, 32 for age prediction, respectively. |