A Recommender System for Mobile Applications of Google Play Store

Abstract

With the growth in the smartphone market, many applications can be downloaded by users. Users struggle with the availability of a massive number of mobile applications in the market while finding a suitable application to meet their needs. Indeed, there is a critical demand for personalized application recommendations. To address this problem, we propose a model that seamlessly combines content-based filtering with application profiles. We analyzed the applications available on the Google Play app store to extract the essential features for choosing an app and then used these features to build app profiles. Based on the number of installations, the number of reviews, app size, and category, we developed a content-based recommender system that can suggest some apps for users based on what they have searched for in the application's profile. We tested our model using a k-nearest neighbor algorithm and demonstrated that our system achieved good and reasonable results.