A developer-oriented recommender model for the app store: A predictive network analytics approach

ABSTRACT

While thousands of new mobile applications (i.e., apps) are being added to the major app markets daily, only a small portion of them attain their financial goals and survive in these competitive marketplaces. A key to the quick growth and success of relatively less popular apps is that they should make their way to the limited list of apps recommended to users of already popular apps;however, the focus of the current literature on consumers has created a void of design principles for app developers. In this study, employing a predictive network analytics approach combined with deep learning-based natural language processing and explainable artificial intelligence techniques, we shift the focus from consumers and propose a developer-oriented recommender model. We employ a set of app-specific and network-driven variables to present a novel approach for predicting potential recommendation relationships among apps, which enables app developers and marketers to characterize and target appropriate consumers. We validate the proposed model using a large (>23,000), longitudinal dataset of medical apps collected from the iOS App Store at two time points. From a total of 10,234 network links (rec-ommendations) formed between the two data collection points, the proposed approach was able to correctly predict 8,780 links (i.e., 85.8 %). We perform Shapley Additive exPlanation (SHAP) analysis to identify the most important determinants of link formations and provide insights for the app developers about the factors and design principles they can incorporate into their development process to maximize the chances of success for their apps.