Paper Info
Title
Tweet-mapping Method for Tourist Spots Based on Now-Tweets and Spot-photos
Abstract
Tourism recommender systems suggest suitable tourist spots by matching the characteristics of the tourist spots with those of the user. In this paper, we focus on an essential source of these characteristics-geotagged tweets. To solve the problem of associating geotagged tweets to tourist spots, we propose a mapping method that infers the region of a target spot on the basis of two geotagged items. The first is a geotagged tweet, which demonstrates that the tweeter was indeed at the target spot at the time the tweet was posted. We call this a "now-tweet." The second item is a geotagged photo of the target spot, which we call a "spot-photo." We regard these now-tweets and spot-photos as training data, and then determine the region of the tourist spot by inferring the geographical distribution of the training data. Next, we map geotagged tweets from the extracted region to the target spot. To improve the accuracy with which the tourist spot is inferred, we apply a clustering algorithm to the training data. Experimental results indicate that photo-based mapping with sophisticated training data produces the most improved performance over baseline methods. When applied to 4,559,643 geotagged tweets, our method maps them to tourist spots with an average granularity of 144.85 m.
Year
DOI
Venue
2015
10.1016/j.procs.2015.08.202
Procedia Computer Science
Keywords
DocType
Volume
Geotagged tweets,Geotagged photos,Tourist spot analysis
Conference
60
ISSN
Citations 
PageRank 
1877-0509
0
0.34
References 
Authors
18
3
Name
Order
Citations
PageRank
Kenta Oku18414.81
Fumio Hattori216426.81
Kyoji Kawagoe313568.78