CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data
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| 發表在: | arXiv.org (Feb 15, 2018), p. n/a |
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| 主要作者: | |
| 其他作者: | , , , , |
| 出版: |
Cornell University Library, arXiv.org
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| 主題: | |
| 在線閱讀: | Citation/Abstract Full text outside of ProQuest |
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| Resumen: | 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 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. |
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| ISSN: | 2331-8422 |
| Fuente: | Engineering Database |