Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |