POI2Vec: Geographical Latent Representation for Predicting Future Visitors
Authors: Shanshan Feng, Gao Cong, Bo An, Yeow Meng Chee
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on 2 real-world datasets to demonstrate the superiority of our proposed approach over the state-of-the-art algorithms for both next POI prediction and future user prediction. |
| Researcher Affiliation | Academia | Shanshan Feng,1 Gao Cong,2 Bo An,2 Yeow Meng Chee3 1Interdisciplinary Graduate School 2 School of Computer Science and Engineering 3School of Physical and Mathematical Sciences Nanyang Technological University, Singapore {sfeng003@e., gaocong@, boan@, ymchee@}ntu.edu.sg |
| Pseudocode | No | The paper describes models and processes using mathematical equations and textual descriptions, but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or include links to a code repository for the described methodology. |
| Open Datasets | Yes | We conduct experiments on two publicly available datasets. The first one is the Foursquare check-ins within Singapore (Yuan et al. 2013a) and the second one is the Gowalla check-ins within Houston (Liu et al. 2013). |
| Dataset Splits | Yes | For both datasets, we use the first 90% chronological check-ins as the training set, the 90 95% as the tuning set, and the last 5% as test set. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used to run the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions techniques like "word2vec" but does not specify any software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | Based on the tuning set, the number of dimensions D = 200, region size threshold θ = 0.1, learning rate is set at 0.005. |