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..
Where and Why Users “Check In”
Authors: Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan
AAAI 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this study we analyze the check-in patterns in LBSN and observe significant temporal clustering of check-in activities... Using check-in data from three major cities, we show not only that our model can improve prediction of future check-ins... For every popular venue, we fit the data to a Hawkes process using the EM algorithm and evaluate the goodness of fit compared against other baseline approaches |
| Researcher Affiliation | Academia | Yoon-Sik Cho, Greg Ver Steeg, and Aram Galstyan USC Information Sciences Institute Marina del Rey, CA 90292 EMAIL, EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We use the Gowalla dataset (Cho, Myers, and Leskovec 2011) in this work. |
| Dataset Splits | Yes | The check-ins made before the mid-point are collected as a training set... For each venue, we repeat the experiment 1,000 times for different random t s and compare our prediction to the actual number of events. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms like EM but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, libraries). |
| Experiment Setup | No | The paper describes the model fitting process but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, epochs) or specific optimizer settings. |