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..
Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation
Authors: Xiaolin Wang, Guohao Sun, Xiu Fang, Jian Yang, Shoujin Wang
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on three real-world datasets demonstrate that the proposed model outperforms the state-of-the-art POI recommendation approaches. |
| Researcher Affiliation | Academia | 1Donghua University 2Macquarie University, School of Computing 3RMIT University 4Shanghai AI Laboratory |
| Pseudocode | Yes | Algorithm 1: STGCAN |
| Open Source Code | Yes | 1https://github.com/greenwangzero/STGCAN |
| Open Datasets | No | The paper mentions using 'three public real-world datasets: NYC, TKY and Gowalla' but does not provide a direct link, DOI, specific repository, or a formal citation for the datasets themselves with author names and year, which is required for concrete access information. |
| Dataset Splits | Yes | The datasets are formed as order sequences by timestamps, where the first [1, m 2) sequences are for training; the (m 2)-nd is for validations the (m 1)-st is for test; the [2, m 2]-th, (m 1)-st, m-th sequences are set as the labels of the three parts respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific version numbers for any ancillary software dependencies (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | For our model1, we set the embedding size to 40, the maximum visit sequence length to 100, the dropout rate of 0.2, and the learning rate of 0.03 in experiments. |