抄録
Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.
| 元の言語 | English |
|---|---|
| ホスト出版物のタイトル | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
| 編集者 | Yang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu |
| 出版者 | Institute of Electrical and Electronics Engineers Inc. |
| ページ | 3628-3637 |
| ページ数 | 10 |
| ISBN（電子版） | 9781538650356 |
| DOI | |
| 出版物ステータス | Published - 2019 1 22 |
| イベント | 2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States 継続期間: 2018 12 10 → 2018 12 13 |
出版物シリーズ
| 名前 | Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 |
|---|
Conference
| Conference | 2018 IEEE International Conference on Big Data, Big Data 2018 |
|---|---|
| 国 | United States |
| 市 | Seattle |
| 期間 | 18/12/10 → 18/12/13 |
Fingerprint
ASJC Scopus subject areas
- Computer Science Applications
- Information Systems
これを引用
Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis. / Bao, Siya; Yanagisawa, Masao; Togawa, Nozomu.
Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018. 版 / Yang Song; Bing Liu; Kisung Lee; Naoki Abe; Calton Pu; Mu Qiao; Nesreen Ahmed; Donald Kossmann; Jeffrey Saltz; Jiliang Tang; Jingrui He; Huan Liu; Xiaohua Hu. Institute of Electrical and Electronics Engineers Inc., 2019. p. 3628-3637 8622103 (Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018).研究成果: Conference contribution
}
TY - GEN
T1 - Landmark Seasonal Travel Distribution and Activity Prediction Based on Language-specific Analysis
AU - Bao, Siya
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2019/1/22
Y1 - 2019/1/22
N2 - Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.
AB - Online media communities have globally spanned and have increasingly accelerated the development of intelligent travel recommendation systems in both academic and industrial fields. However, there is a bottleneck that differences in users' seasonal travel distributions (when to visit) in various language groups are ignored. This paper proposes a seasonal activity prediction algorithm based on user comments over the period of 2012 to 2017 in different language groups. We take the advantage of online user comments which provide visiting time for each landmark and detailed activity description. With the accumulation of 417,787 user comments on TripAdvisor for 300 landmarks in three big cities, we analyze the language-specific differences in travel distributions. After that, prediction of future travel distribution for each language group is generated. Then potential peak and off seasons of each landmark are distinguished and representative seasonal activities are extracted through comment contents for peak and off seasons, respectively. Experimental results in the three cities show that the proposed algorithm is more accurate in terms of peak season detection and seasonal activity prediction than previous studies.
KW - language-specific
KW - peak-off season detection
KW - seasonal activity
KW - travel distribution
UR - http://www.scopus.com/inward/record.url?scp=85062614594&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062614594&partnerID=8YFLogxK
U2 - 10.1109/BigData.2018.8622103
DO - 10.1109/BigData.2018.8622103
M3 - Conference contribution
T3 - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
SP - 3628
EP - 3637
BT - Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
A2 - Song, Yang
A2 - Liu, Bing
A2 - Lee, Kisung
A2 - Abe, Naoki
A2 - Pu, Calton
A2 - Qiao, Mu
A2 - Ahmed, Nesreen
A2 - Kossmann, Donald
A2 - Saltz, Jeffrey
A2 - Tang, Jiliang
A2 - He, Jingrui
A2 - Liu, Huan
A2 - Hu, Xiaohua
PB - Institute of Electrical and Electronics Engineers Inc.
ER -