Robust Spatio-Temporal Purchase Prediction via Deep Meta Learning
Authors: Huiling Qin, Songyu Ke, Xiaodu Yang, Haoran Xu, Xianyuan Zhan, Yu Zheng4312-4319
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the meta-learning generalization ability of STMP. STMP outperforms baselines in all cases, which shows the effectiveness of our model. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science and Technology, Xidian University, Xi an, China 2 JD Intelligent Cities Research, Beijing, China 3 JD i City, JD Technology, Beijing, China 4 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China 5 Artificial Intelligence Institute, Southwest Jiaotong University, China |
| Pseudocode | Yes | Algorithm 1 ST-Training |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code. |
| Open Datasets | No | The paper states, "We use a large-high-quality online purchase dataset from JD.com to evaluate our model." However, it does not provide concrete access information (e.g., a link, DOI, or explicit statement of public availability) for this dataset. JD.com is a commercial entity, implying the dataset is proprietary. |
| Dataset Splits | No | The paper mentions "The training Dr and testing Dr data are distinguished for each task" and describes "1-shot to 4-shot experiments using different numbers of years data (2015 2018)" which implies some data partitioning. However, it does not provide specific details on the train/validation/test splits (e.g., percentages, sample counts, or predefined split citations) needed to reproduce the experiments for the entire dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., Python, deep learning frameworks, or libraries) used in the experiments. |
| Experiment Setup | No | The paper states, "To make a fair comparison, we present the best performance of each method under fine-tuned parameter settings in Table 1," implying hyperparameter tuning was performed. However, it does not explicitly list the specific hyperparameter values or other detailed training configurations within the main text. |