App recommendation based on both quality and security. Issue 3 (16th November 2020)

• Record Type: Journal Article
• Authors: Gao, Shanquan; Liu, Lei; Liu, Yuzhou; Liu, Huaxiao; Wang, Yihui; Liu, Peixun
• Is Part Of: Journal of software. Volume 33:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-11-16
• Subjects: app recommendation -- description mining -- privacy security -- quality of functionality -- review analysis; Software engineering -- Periodicals; Computer software -- Development -- Periodicals; Software maintenance -- Periodicals; 005.1
• DOI: 10.1002/smr.2325
• Languages: English
• ISSNs: 2047-7473

Abstract

Abstract: With the rapid prevalence of smartphones and the dramatic proliferation of mobile applications, people tend to do everything at their fingertips, including some sensitive activities, such as bank transfers. This makes security become one important factor when recommending apps to users. However, most existing methods recommend apps only on the basis of the apps' functionalities. Even when some methods take security into account, they usually roughly group apps with functionalities and identify the products using extra permissions as risky, but this ignores a common phenomenon that these permissions may be used only to achieve the corresponding functionalities. In this paper, we propose an app recommendation method considering both functionalities and security. For functionalities, we summarized them from app descriptions and further evaluated their completion quality in different products by analyzing their related reviews. For security, we cluster apps with similar functionalities and quality and analyze the permissions of apps in a more comparable range. In this way, our method recommends apps with higher completion quality of functionalities and security degree to users according to their demands. We conducted experiments on apps collected from six categories of Google Play, and the results show that our method has a good recommendation effect. Abstract : This work proposes an app recommendation method considering both functionalities and security. We summarized the functionalities of apps from descriptions and further evaluate their completion quality in different products based on user reviews. We further cluster apps with similar functionalities and quality to analyze the security of apps in a more comparable range. In this way, our method recommends apps with higher completion quality of functionalities and security degree to users according to their demands.