Abstract
Location-based services often use only a single mobility data source, which typically will be scarce for any new user when the system starts out. We propose a transfer learning method to characterize the temporal distribution of places of individuals by using an external, additional, large-scale check-in data set such as Foursquare data. The method is applied to the next place prediction problem, and we show that the incorporation of additional data through the proposed method improves the prediction accuracy when there is a limited amount of prior data.
Original language | English |
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Title of host publication | Proceedings of the 7th International Conference on Weblogs and Social Media, ICWSM 2013 |
Publisher | AAAI PRESS |
Pages | 701-704 |
Number of pages | 4 |
Publication status | Published - 2013 |
MoE publication type | A4 Article in a conference publication |
Event | International AAAI Conference on Web and Social Media - Cambridge, United States Duration: 8 Jul 2013 → 11 Jul 2013 Conference number: 7 |
Conference
Conference | International AAAI Conference on Web and Social Media |
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Abbreviated title | ICWSM |
Country/Territory | United States |
City | Cambridge |
Period | 08/07/2013 → 11/07/2013 |