Low-Pass Graph Convolutional Network for Recommendation
Authors: Wenhui Yu, Zixin Zhang, Zheng Qin8954-8961
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on two real-world datasets demonstrate the effectiveness of the proposed model. Codes are available on https://github.com/Wenhui-Yu/LCFN. |
| Researcher Affiliation | Collaboration | 1Alibaba Group, 2Tsinghua University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes are available on https://github.com/Wenhui-Yu/LCFN. |
| Open Datasets | Yes | Comprehensive experiments on two real-world datasets demonstrate the effectiveness of the proposed model. We strictly follow the experiment settings of Yu and Qin (2020). For details about the datasets, baselines, and tuning strategies, please see Yu and Qin (2020). |
| Dataset Splits | Yes | F1-score@2 is reported on the validation set of Amazon. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We follow He et al. (2020) to initialize the experimental settings of the experiments in this section, that is: no transformation; no activation; sum pooling; inner product for prediction; BPR loss; Adam as optimizer. |