Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?
Abstract
Much work has been done on understanding and predicting human mobility in time. In this work, we are interested in obtaining a set of users who are spatio-temporally most similar to a query user. We propose an efficient way of user data representation called Spatio-Temporal Signatures to keep track of complete record of user movement. We define a measure called Spatio-Temporal similarity for comparing a given pair of users. Although computing exact pairwise Spatio-Temporal similarities between query user with all users is inefficient, we show that with our hybrid pruning scheme the most similar users can be obtained in logarithmic time with in a (1+\epsilon) factor approximation of the optimal. We are developing a framework to test our models against a real dataset of urban users.
Cite
Text
Shukla et al. "Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9712Markdown
[Shukla et al. "Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/shukla2015aaai-spatio/) doi:10.1609/AAAI.V29I1.9712BibTeX
@inproceedings{shukla2015aaai-spatio,
title = {{Spatio-Temporal Signatures of User-Centric Data: How Similar Are We?}},
author = {Shukla, Samta and Telang, Aditya and Joshi, Salil and Subramaniam, L. Venkata},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2015},
pages = {4208-4209},
doi = {10.1609/AAAI.V29I1.9712},
url = {https://mlanthology.org/aaai/2015/shukla2015aaai-spatio/}
}