POISketch: Semantic Place Labeling over User Activity Streams
Authors: Dingqi Yang, Bin Li, Philippe Cudré-Mauroux
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation on real-world datasets demonstrates the validity of our approach and shows that sketches can be efficiently and effectively used to infer place labels over user activity streams. |
| Researcher Affiliation | Academia | 1e Xascale Infolab, University of Fribourg, 1700 Fribourg, Switzerland 2Data61, CSIRO, Eveleigh NSW 2015, Australia |
| Pseudocode | No | The paper describes methods textually but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | No concrete access (e.g., specific repository link, explicit statement of code release) to the source code for the methodology was found. |
| Open Datasets | Yes | We evaluate our approach on a check-in dataset collected by [Yang et al., 2015a; 2016] for about 18 months (from April 2012 to September 2013). Without loss of generality, we select check-in data from two big cities, New York City and Tokyo, for our experiments. |
| Dataset Splits | No | The paper describes a training and testing split (first 9 months for training, last 9 months for testing) but does not explicitly mention a separate validation split with specific details. |
| Hardware Specification | Yes | All experiments were conducted on a commodity PC (Intel Core i7-4770HQ@2.20GHz, 16GB RAM, Mac OS X) running MATLAB3 version 2014b. |
| Software Dependencies | Yes | All experiments were conducted on a commodity PC (Intel Core i7-4770HQ@2.20GHz, 16GB RAM, Mac OS X) running MATLAB3 version 2014b. |
| Experiment Setup | Yes | We empirically set KNN with the five nearest neighbors, and report the classification accuracy on both the 9 root categories (Lv1) and the 291 sub-categories (Lv2). The classification is triggered when 10 check-ins are observed for each POI tested. We set the parameters d = 10, w = 50 to guarantee an error within 4% with probability 0.999. In this experiment, we compare our method to the baseline approaches by fixing the sketch length to 50 for all sketching methods. |