Name
Abstract | ||
|---|---|---|
Nowadays routing systems can provide optimal routes in terms of time and travel distance. However, they do not consider special needs of certain group of users. For example, people recovering from alcohol and drug addiction may want to travel a route that is alcohol and drug-free. In this demonstration, we propose a system we built that helps with this special need. We detect if a street is related to alcohol and drug by exploiting Web open data, including Foursquare, microblog tweets, Google Street View images, and crime data. We calculate an alcohol and drug relevance score using unsupervised methods, to be used in route ranking. Our system prototype is ready to be tested for the cities of San Francisco and Kyoto.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3357843 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
customized routing, web information system, web open data | World Wide Web,Information retrieval,Computer science,Alcohol,Drug | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yihong Zhang | 1 | 9 | 10.65 |
| Panote Siriaraya | 2 | 42 | 15.50 |
| Yukiko Kawai | 3 | 188 | 43.43 |
| Adam Jatowt | 4 | 903 | 106.73 |