Name
Abstract | ||
|---|---|---|
Most location sharing applications display people's locations on a map. However, people use a rich variety of terms to refer to their locations, such as "home," "Starbucks," or "the bus stop near my house." Our long-term goal is to create a system that can automatically generate appropriate place names based on real-time context and user preferences. As a first step, we analyze data from a two-week study involving 26 participants in two different cities, focusing on how people refer to places in location sharing. We derive a taxonomy of different place naming methods, and show that factors such as a person's perceived familiarity with a place and the entropy of that place (i.e. the variety of people who visit it) strongly influence the way people refer to it when interacting with others. We also present a machine learning model for predicting how people name places. Using our data, this model is able to predict the place naming method people choose with an average accuracy higher than 85%. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1864349.1864362 | UbiComp |
Keywords | Field | DocType |
appropriate place name,different place,method people,rich variety,average accuracy,different city,people name place,bus stop,location sharing,long-term goal,machine learning,representation,real time,location based service | Toponymy,World Wide Web,Computer science,Location-based service,Human–computer interaction,Location sharing | Conference |
Citations | PageRank | References |
39 | 1.73 | 34 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lin, Jialiu | 1 | 936 | 39.17 |
| Guang Xiang | 2 | 382 | 18.31 |
| Jason Hong | 3 | 6706 | 518.75 |
| Norman M. Sadeh | 4 | 3472 | 253.13 |