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 | Conference PDF | Archive PDF | Plain Text | 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. |