Where and Why Users “Check In”
Authors: Yoon-Sik Cho, Greg Ver Steeg, Aram Galstyan
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | 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 yoonsik@isi.edu, gregv@isi.edu, galstyan@isi.edu |
| 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. |