CompetitiveBike Competitive Analysis and Popularity Prediction of Bike-Sharing Apps Using Multi-Source Data

 CompetitiveBike Competitive Analysis and Popularity Prediction of Bike-Sharing Apps Using Multi-Source Data

ABSTRACT

 

 

In recent years, bike-sharing systems have been widely deployed in many big cities, which provide an economical and healthy lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the bike-sharing market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions and predict the popularity of 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 leveraging multi-source data. We extract two novel types of features: coarse-grained and fine-grained competitive

features, and utilize Random Forest model to forecast the future competitiveness. In addition, we view mobile apps competition as a long-term event and generate the event storyline to enrich our competitive analysis. We collect data about two bike-sharing apps and two food ordering & delivery apps from 11 app stores and Sina Weibo, implement extensive experimental studies, and the results demonstrate the effectiveness and generality of our approach.

EXISTING SYSTEM

 

v    Recently, a significant effort has been spent on predicting popularity of mobile app [22], [23], [24], [25], [26], [27], [28]. Zhu et al. [22] proposed the Popularity-based Hidden Markov Model (PHMM) to model the popularity information of mobile apps, which can learn the sequences of heterogeneous popularity observations from mobile Apps. Wang et al. [23] proposed a hierarchical model to forecast the app downloads.

v    Ghose et al. [24] estimated demand for mobile apps by using econometric model. Malmi et al. [25] found that there existed connection between app popularity and the past popularity of other apps from the same publisher. Finkelstein et al. [26] extracted a set of features from release notes available in app store, and found that there is a strong correlation between customer rating and the number of downloads. Garg et al. [27] inferred app downloads based on the rank of the app. Girardello et al. [28] presented AppAware, a platform for discovering mobile apps based on their current popularity.

 

Disadvantages

o   In the existing work, to the best of our knowledge, the problem of predicting the competitiveness of mobile apps has not been well investigated in the literature..

There is no accurate result in analyzing Bike Micro blog data due to poor techniques

PROPOSED SYSTEM

 

v The system proposes CompetitiveBike, a system that predicts the outcomes of the popularity contest among bike-sharing apps leveraging app store data and microblogging data, and then generates the event storyline of the contest. We first obtain app descriptive statistics and sentiment information from app store data, and descriptive statistics and comparative information from microblogging data. Using these data, we extract both coarse-grained and fine-grained competitive features, we then train a regression model to predict the outcomes of popularity contest. We finally generate the event storyline to provide competitive analysis and present the popularity contest. In summary, we make the following contributions.

v This work is the first to study the problem of competitive analysis and popularity contest of bike-sharing apps. We use two indicators for the comparison: competitive relationship to indicate which app is more popular; and competitive intensity to measure the popularity gap between the two apps/systems.

v To predict popularity contest, we extract features from different aspects including inherent descriptive information of apps, users’ sentiment, and comparative opinions. With this information, we further extract two novel features: coarse-grained and fine-grained competitive features, and choose Random Forest algorithm for prediction.

v To provide competitive analysis, we utilize topic model to analyze the topics in competing apps, and apply the minimum-weight dominating algorithm to select representative microblogs. We also generate event storyline to present and visualize the popularity contest.

v To evaluate CompetitiveBike, we collect data about Mobike and Ofo from 11 mobile app stores and Sina Weibo. With the data collected, we conduct extensive experiments from different perspectives. We find that the Random Forest model performs well on competitive relationship prediction (the Accuracy is 71.4%) as well as competitive intensity prediction (the RMSE is 0.1886). A combination of the coarse-grained and fine-grained competitive features improves performance in popularity contest prediction, and a combination of data from app store and microblogging also improves performance in popularity contest prediction. Besides, we collect data about two mobile food ordering & delivery apps, the results with extensive experiments demonstrate the effectiveness and generality of our method.

Advantages

 

 

Ø To thrive in the competitive market, it is vital for bike sharing companies and app developers to understand their competitors, and then make strategic decisions for mobile app development and evolution.

Specifically, users may upload their requirements (e.g.functional requirements), preferences (e.g. UI preferences) or sentiment (e.g. positive, negative) through reviews, as well as their satisfaction level through ratings. Therefore, the app store data can reflect users’ online experience with the app. Online social media is another way to share the user experience of a mobile app.

Modules

Service Provider

 

In this module, the Service Provider has to login by using valid user name and password. After login successful he can do some operations such as          Add Bike App details,View all uploaded App Details,View Positive Reviews- Popularity Predictions,View all negative reviews,View all neutral reviews,View App Rating Results,View Dislike results,View Like Results,View all remote users,View all Apps reviews,View Trending app news,View all shared app details.

View and Authorize Users

In this module, the admin can view the list of users who all registered. In this, the admin can view the user’s details such as, user name, email, address and admin authorizes the users.

 

Remote User

In this module, there are n numbers of users are present. User should register before doing any operations. Once user registers, their details will be stored to the database.  After registration successful, he has to login by using authorized user name and password. Once Login is successful user will do some operations like   View all Bike Apps Details,View all apps reviews,View all Popular App,View your profile,View all shared details.

Author : ss infotech

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