Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation
Authors: Lianghao Xia, Chao Huang, Yong Xu, Peng Dai, Xiyue Zhang, Hongsheng Yang, Jian Pei, Liefeng Bo4486-4493
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on three real-world datasets show that KHGT consistently outperforms many state-of-the-art recommendation methods across various evaluation settings. |
| Researcher Affiliation | Collaboration | 1South China University of Technology, China, 2JD Finance America Corporation, USA 3Communication and Computer Network Laboratory of Guangdong, China 4Peng Cheng Laboratory, Shenzhen, China, 5Simon Fraser University, Canada |
| Pseudocode | No | The paper describes the model architecture and methodology in text and figures, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation code is available in https://github.com/akaxlh/KHGT. |
| Open Datasets | Yes | Data Description. We show the data statistics in Table 1. Movie Lens Data. We differentiate explicit user s rating scores r (i.e., [1, ..., 5]) into multiple behavior types... Yelp Data. User s feedbacck interactions over items in this data, are projected into three behavior types... Online Retail Data. We also investigate our KHGT in a real-world online retail scenario with explicit multi-typed user-item interactions... |
| Dataset Splits | No | Following the same settings in (Yu et al. 2019; Zhao et al. 2020), we employ the time-aware leave-one-out evaluation to split the training/test sets. The test set contains the last interactive item of users and the rest of data is used for training. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU or GPU models, or cloud computing instance types, used for running the experiments. |
| Software Dependencies | No | The paper states 'We implement KHGT with Tensor Flow' but does not provide specific version numbers for TensorFlow or any other software dependencies. |
| Experiment Setup | Yes | Our multi-head self-attention module is configured with the 2 heads for embedding learning. The channels of base transformations in our graph attention module is set as 2. The model training process is performed with the learning rate of 1e 3 (with decay rate of 0.96) and batch size of 32. The regularization strategy with the weight decay, which is chosen from the set of {0.1, 0.05, 0.01, 0.005, 0.001}. |