Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
Authors: Xin Xia, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, Xiangliang Zhang4503-4511
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the results validate the effectiveness of hypergraph modeling and self-supervised task. |
| Researcher Affiliation | Academia | 1The University of Queensland, 2Shandong University, 3King Abdullah University of Science and Technology {x.xia, h.yin1, jl.yu, qinyong.wang}@uq.edu.au, clz@sdu.edu.cn, xiangliang.zhang@kaust.edu.sa |
| Pseudocode | No | The paper includes Figure 1 illustrating the model pipeline but does not provide pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation of our model is available via https://github.com/xiaxin1998/DHCN. |
| Open Datasets | Yes | We evaluate our model on two real-world benchmark datasets: Tmall2, Nowplaying3 and Diginetica4. Tmall dataset comes from IJCAI-15 competition, which contains anonymized user s shopping logs on Tmall online shopping platform. Nowplaying dataset describes the music listening behavior of users. For both datasets, we follow (Wu et al. 2019b; Li et al. 2017) to remove all sessions containing only one item and also remove items appearing less than five times. To evaluate our model, we split both datasets into training/test sets, following the settings in (Wu et al. 2019b; Li et al. 2017; Wang et al. 2020c). Then, we augment and label the dataset by using a sequence splitting method, which generates multiple labeled sequences with the corresponding labels ([is,1], is,2), ([is,1, is,2], is,3), ..., ([is,1, is,2, ..., is,m 1], is,m) for every session s = [is,1, is,2, is,3, ..., is,m]. Note that the label of each sequence is the last click item in it. The statistics of the datasets are presented in Table 1. |
| Dataset Splits | No | The paper mentions splitting data into "training/test sets" and references mini-batches during training, but does not explicitly state details about a distinct validation set split or its use. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python version, library versions like PyTorch). |
| Experiment Setup | Yes | For the general setting, the embedding size is 100, the batch size for mini-batch is 100, and the L2 regularization is 10−5. For DHCN, an initial learning rate 0.001 is used. The number of layers is different in different datasets. For Nowplaying and Diginetica, a three-layer setting is the best, while for Tmall, one-layer setting achieves the best performance. |