Collaborative Place Models
Authors: Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply CPM to both sparse and dense datasets, and demonstrate how it both improves location prediction performance and provides new insights into users spatiotemporal patterns. |
| Researcher Affiliation | Collaboration | Berk Kapicioglu Foursquare Labs... David S. Rosenberg YP Mobile Labs... Robert E. Schapire Princeton University... Tony Jebara Columbia University |
| Pseudocode | Yes | Algorithm 1 Collapsed Gibbs sampler for CPM. |
| Open Source Code | No | The paper states 'Supplements are available at http://www.berkkapicioglu.com.' but does not explicitly or unambiguously state that the source code for the described methodology is provided at this link. |
| Open Datasets | No | The paper uses 'a dense cellular carrier dataset and a sparse mobile ad exchange dataset' but does not provide concrete access information (link, DOI, repository, or formal citation) for these datasets, implying they are not publicly available. |
| Dataset Splits | Yes | for each user, split the user s data points into 3 partitions: earliest 60% is added to the training data, middle 20% to the validation data, and final 20% to the test data. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | We set the hyperparameters by choosing the number of factors from F {1, . . . , 10} and the Dirichlet parameters from α, β {0.01, 0.1, 1, 10}. |