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| Content Provider | ACM Digital Library |
|---|---|
| Author | Jameel, Shoaib Schockaert, Steven Xie, Xing Liao, Yi Lam, Wai |
| Abstract | We propose a new unsupervised nonparametric temporal topic model to discover lifestyle patterns from location-based social networks. By relating the textual content, time stamps, and venue categories associated to user check-ins, our framework detects the predominant lifestyle patterns in a given geographic region. The temporal component of our model allows us to analyse the evolution of lifestyle patterns throughout the year. We provide examples of interesting patterns that have been discovered by our model, and we show that our model compares favourably to existing approaches in terms of lifestyle pattern quality and computation time. We also quantitatively show that our model outperforms existing methods in a time stamp prediction task. |
| Starting Page | 251 |
| Ending Page | 260 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450344975 |
| DOI | 10.1145/2970398.2970401 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-09-12 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Text mining Bayesian nonparametrics Lifestyle patterns Twitter Urban computing Gibbs sampling Graphical models Lbsn Topic models Location-based social networks Posterior inference Foursquare Online social networks |
| Content Type | Text |
| Resource Type | Article |
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