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..
POI2Vec: Geographical Latent Representation for Predicting Future Visitors
Authors: Shanshan Feng, Gao Cong, Bo An, Yeow Meng Chee
AAAI 2017 | Venue PDF | 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. |