LoCaTe: Influence Quantification for Location Promotion in Location-based Social Networks
Authors: Ankita Likhyani, Srikanta Bedathur, Deepak P
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our model on a long-term crawl of Foursquare data collected between Jan 2015 Feb 2016, as well as on publicly available LBSN datasets. Our experiments demonstrate that Lo Ca Te significantly outperforms state-of-the-art models for the same task. |
| Researcher Affiliation | Collaboration | Ankita Likhyani Indraprastha Institute of Information Technology, Delhi, India ankital@iiitd.ac.in Srikanta Bedathur IBM-Research Lab, Delhi, India sbedathur@in.ibm.com Deepak P. Queen s University Belfast Northern Ireland, UK deepaksp@acm.org |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code and the datasets used are available publicly 6. [Footnote 6: https://goo.gl/ayzehx] |
| Open Datasets | Yes | We tested over 5 datasets as shown in table 3, of which FSq 16 is the one that we collected using Twitter and Four Square APIs, and rest are publicly available datasets [Cho et al., 2011; Gao et al., 2012]. |
| Dataset Splits | Yes | All data prior to the cut-off timestamp is used as training data to learn the model. The remaining data forms the test set against which the effectiveness of the learned model is measured. ... The cut-off timestamp is chosen such that 80% of total checkins are used for training. ... We also construct a validation set in the same manner as test set is built from the training set for learning the parameters βv and α... |
| Hardware Specification | Yes | We ran the code on a 6-core 2.5GHz Intel Xeon CPU with 64GB of RAM. |
| Software Dependencies | No | The paper mentions using 'UCI Datalab website' for KDE and 'http://mallet.cs. umass.edu/topics-devel.php' for LDA, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The parameter Z (number of topics) is set to 50. ... Table 4 shows values of βv and α : learned for different datasets. |