TY - GEN
T1 - CompetitiveBike
T2 - 13th International Conference on Green, Pervasive, and Cloud Computing, GPC 2018
AU - Ouyang, Yi
AU - Guo, Bin
AU - Lu, Xinjiang
AU - Han, Qi
AU - Guo, Tong
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - In recent years, bike-sharing systems have been deployed in many cities, which provide an economical lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions for mobile apps development. Therefore, it is significant to predict and compare the popularity of different bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been well explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps. Moreover, we conduct experiments on real-world datasets collected from 11 app stores and Sina Weibo, and the experiments demonstrate the effectiveness of our approach.
AB - In recent years, bike-sharing systems have been deployed in many cities, which provide an economical lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions for mobile apps development. Therefore, it is significant to predict and compare the popularity of different bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been well explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps. Moreover, we conduct experiments on real-world datasets collected from 11 app stores and Sina Weibo, and the experiments demonstrate the effectiveness of our approach.
KW - Bike-sharing app
KW - Competitive prediction
KW - Crowdsourced data
KW - Mobile app
KW - Popularity contest
UR - http://www.scopus.com/inward/record.url?scp=85064044439&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-15093-8_17
DO - 10.1007/978-3-030-15093-8_17
M3 - 会议稿件
AN - SCOPUS:85064044439
SN - 9783030150921
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 241
EP - 255
BT - Green, Pervasive, and Cloud Computing - 13th International Conference, GPC 2018, Revised Selected Papers
A2 - Li, Shijian
PB - Springer Verlag
Y2 - 11 May 2018 through 13 May 2018
ER -