Predicting Temporal Sets with Simplified Fully Connected Networks
Authors: Le Yu, Zihang Liu, Tongyu Zhu, Leilei Sun, Bowen Du, Weifeng Lv
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four benchmarks show the superiority of our approach over the state-of-the-art under both transductive and inductive settings. We also theoretically and empirically demonstrate that our model has lower space and time complexity than baselines. Codes and datasets are available at https://github.com/yule-BUAA/SFCNTSP. |
| Researcher Affiliation | Academia | State Key Laboratory of Software Development Environment, Beihang University, Beijing, 100191, China {yule,lzhmark,zhutongyu,leileisun,dubowen,lwf}@buaa.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Codes and datasets are available at https://github.com/yule-BUAA/SFCNTSP. |
| Open Datasets | Yes | Descriptions of Benchmarks Following Yu et al. (2022), we use four benchmarks in the experiments, including Jing Dong, DC, Tao Bao and TMS. Jing Dong3 records the actions of users about purchasing, browsing, following, commenting, and adding products to shopping carts. 3https://jdata.jd.com/html/detail.html?id=8 Dunnhumby-Carbo (DC)4 includes the transactions of households at a retailer in two years. 4https://www.dunnhumby.com/careers/engineering/sourcefiles Tao Bao5 contains the online user behaviors about purchasing, clicking, marking products as favors, and adding products to shopping carts. 5https://tianchi.aliyun.com/dataset/data Detail?data Id=649 Tags-Math-Sx (TMS)6 contains the history of users questions in Mathematics Stack Exchange and we use the preprocessed version in Yu et al. (2022) in experiments. 6https://math.stackexchange.com |
| Dataset Splits | Yes | For the transductive setting, we follow Yu et al. (2022) to use the last set, the second last set, and the remaining sets of each user for testing, validation, and training. For the inductive setting, we follow Yu et al. (2020) to randomly split each dataset across users with the ratio of 70%, 10%, and 20% for training, validation, and testing. |
| Hardware Specification | Yes | We conduct the experiments on an Ubuntu machine equipped with one Intel(R) Xeon(R) Gold 5218 CPU @ 2.30GHz with 16 CPU cores. The GPU device is NVIDIA Ge Force RTX 3090 with 24 GB memory. |
| Software Dependencies | No | Our model is implemented by Py Torch (Paszke et al. 2019). While PyTorch is mentioned, no specific version number is provided for PyTorch or any other software dependency. |
| Experiment Setup | Yes | We set the learning rate and batch size to 0.001 and 64 on all the datasets. We search the dropout rate and the number of embedding channels c in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3] and [32, 64, 128]. For hyperparameters α and β, we set α to 1.0 to represent the residual connection and search β in [0.0, 0.01, 0.1, 0.3, 0.5]. The configurations of our model under the transductive setting are shown in Table 3. |