Modeling Spatio-temporal Neighbourhood for Personalized Point-of-interest Recommendation
Authors: Xiaolin Wang, Guohao Sun, Xiu Fang, Jian Yang, Shoujin Wang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | 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. |