CompetitiveBike: Competitive Prediction of Bike-Sharing Apps Using Heterogeneous Crowdsourced Data
This addresses the need for bike-sharing companies and app developers to make strategic decisions in a competitive market, though it is incremental as it extends existing single-app prediction to multi-app contests.
The paper tackles the problem of predicting popularity contests between bike-sharing apps, specifically forecasting the competition between Mobike and Ofo in China, and demonstrates the effectiveness of their CompetitiveBike system using real-world datasets from 11 app stores and Sina Weibo.
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.