App Store Analysis: Using Regression Model for App Downloads Prediction

Wang, Shanshan; Wu, Wenjun; Zhou, Xuan

doi:10.1007/978-981-10-2053-7_19

Cited by 3 publications

(4 citation statements)

References 10 publications

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“…A modeling process ranging from artificial intelligence, statistics, and machine learning is always available in prediction analysis. An accurate RBP for app downloads which can help developers optimize some factors that influence apps can be found in [4]. The model is considered based on measurement, verification, and evaluation, employing the detection technique to estimate the effects from input data.…”

Section: Related Studiesmentioning

confidence: 99%

“…A simulation for the mobile application to rank algorithms in the app store has been investigated in [11]. The paper differs from [4], wherein a multiple regression-based prediction (MRBP) is opted to outline a dependent variable and three related attributes (independent variables). The multiple regression-based predictive model is a process of creating a continuous random dependent variable (also called the response variable), Y, and a number of independent variables, X1, X2, .…”

Section: Related Studiesmentioning

confidence: 99%

“…These differences can be calculated by the sample population overestimation errors sometimes called an out-of-sample figure. The RMSE [21] of estimated values ̂ for times t of a regression variable can be computed for n different estimations as listed in (4). Training a dataset will minimize the error rate for the experiment set.…”

Section: Rmsementioning

confidence: 99%

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Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Jittawiriyanukoon¹

2019

IJEECS

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<span>Multiple Regression-Based Prediction (MRBP) is an emerging calculation to or analysis technique cope with the future by compiling the history of data. The MRBP characteristic will include an approximation for the associations between physical observations and predictions. MRBP is a predictive model, which will be an important source of knowledge in terms of an interesting trend to be followed in the future. However, there is impairment in the MRBP dataset, wherein each form of missing and noisy data has caused an error and is unavailable further analysis. To overcome this unavailability, so that the data analytics can be moved on, two treatment approaches are introduced. First, the given dataset is denoised; next, listwise deletion (LD) is proposed to handle the missing data. The performance of the proposed technique will be investigated by dealing with datasets that cannot be executed. Employing the Massive Online Analysis (MOA) software, the proposed model is investigated, and the results are summarized. Performance metrics, such as mean squared error (MSE), correlation coefficient (COEF), mean absolute error (MAE), root mean squared error (RMSE), and the average error percentage, are used to validate the proposed mechanism. The proposed LD projection is confirmed through actual values. The proposed LD outperforms other treatments as it only requires less state space, which reflects low computation cost, and proves its capability to overcome the limitation of analysis.</span>

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Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

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Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Jittawiriyanukoon¹

2019

IJEECS

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“…For example, Burgers et al ( 2016 ) found that the positive valence (emotion) of online reviews was positively correlated with APP downloads. Through Spearman's correlation analysis, Wang et al ( 2016 ) found out that there was a strong correlation between APP name scores, APP rankings and APP downloads. In fact, online review data contains a lot of valued information, which not only reflects users' emotional inclination and satisfaction with APP products, but also contains valuable user needs information.…”

Section: Introductionmentioning

confidence: 99%

Grounded Theory-Based User Needs Mining and Its Impact on APP Downloads: Exampled With WeChat APP

Chen

Zhang²,

Yang³

et al. 2022

Front. Psychol.

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Software development is an iterative process from designing to implementation, and to testing, in which product development staff should be closely integrated with users. Satisfying user needs effectively is often the pain point for developers. In order to alleviate this, this paper manages to establish the quantitative connection between users' online reviews and APP (Application Program) downloads. By analyzing user online comments, companies can dig out user needs and preferences. This could benefit them by making accurate market positioning of their APP products, and therefore iteratively innovating products based on user needs, which hopefully will increase the volume of APP downloads. This paper regards WeChat APP during 47 updates periods as the research object. Based on Grounded Theory, user needs are extracted after data cleaning. Next, by using semantic analysis and word frequency analysis, we are able to obtain the implicit feedbacks such as emotion tendency, satisfaction and requirements lie under online reviews. Then, we construct a quantile regression model to study the impact of users' online reviews on downloads based on the influencing factors we extracted so as to provide a decision basis for enterprises to iteratively update their products. Results show that: (1) Generally speaking, needs of WeChat users mainly focus on performance, reliability, usability, functional deficiency, functional insufficiency, and system adaptability; (2) For those APP versions with relatively fewer downloads, user needs are mostly about functional deficiency, followed by functional insufficiency, performance, usability, and system adaptability. At this stage, it is found out that users' emotion tendency and user satisfaction significantly affect the volume of downloads; (3) When the volume of APP downloads is moderate, the user needs are functional deficiency, functional insufficiency, and system adaptability. While under this circumstances, users' star ratings have a significant impact on downloads; (4) In addition, when the volume of App downloads is high, user needs are performance, usability, and system adaptability. Our methods effectively extract users' requirements from online reviews and then successfully build up the quantitative connection between the implicit feedbacks from those requirements and APP downloads.

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Conducting Online Surveys in China

Mei

Brown

2017

Social Science Computer Review

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Using online surveys is becoming increasingly extensive and widespread. Social science research in China is no exception. However, due to contextual factors (e.g., technological constraints, social and cultural norms, and language barriers), prior successful methods may not apply. This article reports an alternative way of conducting online surveys in China, by combining local commercial online survey service providers with indigenous Web 2.0 applications. The case study demonstrates the feasibility of this approach and provides practical advice (e.g., adding incentives) on how to effectively conduct online survey in China.

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App Store Analysis: Using Regression Model for App Downloads Prediction

Cited by 3 publications

References 10 publications

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Performance evaluation of listwise deletion for impaired datasets in multiple regression-based prediction

Grounded Theory-Based User Needs Mining and Its Impact on APP Downloads: Exampled With WeChat APP

Conducting Online Surveys in China

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