Name
Abstract | ||
|---|---|---|
We are developing a recommender system for tourist spots. The challenge is mainly to characterize tourist spots whose features change dynamically with trends, events, season, and time of day. Our method uses a one-class support vector machine (OC-SVM) to detect the regions of substantial activity near target spots on the basis of tweets and photographs that have been explicitly geotagged. A tweet is regarded as explicitly geotagged if the text includes the name of a target spot. A photograph is regarded as explicitly geotagged if the title includes the name of a target spot. To characterize the tourist spots, we focus on geotagged tweets, which are rapidly increasing on the Web. The method takes unknown geotagged tweets originating in activity regions and maps these to target spots. In addition, the method extracts features of the tourist spots on the basis of the mapped tweets. Finally, we demonstrate the effectiveness of our method through qualitative analyses using real datasets on the Kyoto area. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/IIAI-AAI.2014.159 | IIAI-AAI |
Keywords | Field | DocType |
internet,feature extraction,recommender systems,social networking (online),support vector machines,travel industry,kyoto area,oc-svm,web,feature extraction method,geotagged tweet mapping,one-class support vector machine,photograph geotagging,substantial activity region detection,tourist spots,geographical recommender systems,geotagged-tweets,user generated content,vectors | Recommender system,User-generated content,Data mining,Spots,Time of day,Computer science,Support vector machine,Tourism,Feature extraction | Conference |
Citations | PageRank | References |
4 | 0.41 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oku, K. | 1 | 4 | 0.41 |
| Ueno, K. | 2 | 4 | 0.41 |
| Fumio Hattori | 3 | 164 | 26.81 |