Recommendation System by Using Android App
Keywords:
Content Based Filtering, Recommendation Systems, Unsupervised Machine Learning, Android ApplicationsAbstract
Due to the expansion of smartphones and the App stores, the number of mobile applications is exponentially growing. Users can download a variety of Apps that offer useful services for practically every part of modern life, including socialising, listening to music, watching videos, and browsing the web, to name a few. The current Google and Google Play store recommendation system is said to make suggestions for apps that are similar to the target application while also taking into account the popularity of each app. However, it does not account for the security features of each programme or the user's preferences. End users can access a wide variety of mobile applications (or apps) through app stores. These apps typically produce network traffic, which uses up users' mobile data plans and could potentially pose a security risk. Due to the lack of a standardised measuring methodology, it is currently difficult to understand how much and what kind of network traffic a mobile app produces in the real world. In this paper, we quantify and examine the network traffic costs associated with Android apps available in the official Android stores. Our analysis of the data reveals that the traffic costs for apps in various categories vary. Regarding the cost of network traffic, there is a notable variation among the apps with comparable functionality. Then, in contrast to traditional app recommendation methods, we incorporate measurements of traffic cost into our algorithm for app recommendation. According to experimental findings, the recommended recommendation algorithm can successfully guide mobile app users away from a number of potential security and privacy problems brought on by the unneeded network traffic consumption.
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