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Activity duration analysis for context-aware services using foursquare check-ins
| Content Provider | ACM Digital Library |
|---|---|
| Author | Brdiczka, Oliver Melià-Seguí, Joan Bart, Eugene Price, Bob Zhang, Rui |
| Abstract | Location-based Social Networks (LBSN) such as Foursquare are becoming an increasingly popular social media, where users share their location and activities with other users mainly using smartphones and Internet of Things. Data logged by LBSNs, such as Foursquare user check-in events, can be used to derive user models and improve the context-awareness and efficiency of various applications like recommender systems. In particular, activity duration is one important aspect of user behavior that we can derive from LBSNs to improve the timing of recommendation sent to users. These durations are not otherwise directly available from LBSNs, partial GPS-enabled tracking or explicit recording due to various practical constraints. From a Four-square dataset with about 3.7 million users and 300 million check-ins, we observed patterns which inspired us to design methods to determine the duration of user activities. We discuss preliminary results and outline plans for thorough evaluations and future research. |
| Starting Page | 13 |
| Ending Page | 18 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450317535 |
| DOI | 10.1145/2378023.2378027 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-09-17 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Contextual intelligence |
| Content Type | Text |
| Resource Type | Article |
