A Mobile Application Recommendation Framework by Exploiting Personal Preference with Constraints

Abstract

Explosive mobile applications (Apps) are proliferating with the popularity of mobile devices (e.g., smartphones, tablets). These Apps are developed to satisfy different function needs of users. Majority of existing App Stores have difficulty in recommending proper Apps for users. Therefore, it is of significance to recommend mobile Apps for users according to personal preference and various constraints of mobile devices (e.g., battery power). In this paper, we propose a mobile App recommendation framework by incorporating different requirements from users. We exploit modern portfolio theory (MPT) to combine the popularity of mobile Apps, personal preference, and mobile device constraints for mobile App recommendation. Based on this framework, we discuss the recommendation approaches by constraints of phone power and limited mobile data plan. Extensive evaluations show that the proposed mobile App recommendation framework can well adapt to power and network data plan constraints. It satisfies the user App preference and mobile device constraints.

Factors Influencing Continued Usage Behavior on Mobile Health Applications

“…The popularity of mobile devices has promoted the development of various apps that provide people with a wide range of services [ 14 , 15 ]. Considering that people are paying more attention to health, mHealth apps have developed into an important tool for users to solve health problems [ 16 , 17 ].…”

Deep learning pipeline for image classification on mobile phones

“…There are already several contexts in which deep learning or other classification models are deployed on mobile phones, including small-scale applications such as emoji selection from text [25] to larger-scale recommendation systems [26,27] and face detection from camera images [28,29]. Implementing image classification presents many challenges, such as ensuring that the image dimensions are the same for training and testing data.…”

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

“…To overcome data sparseness, model-based recommendation methods have been proposed. The most popular method is based on matrix factorization recommendation [2], [4], [5], [9]- [12], [16], [17], [19]- [21], [23], [25]- [27], [30], [34], which decomposes the user-app, M =R m * n two-dimensional matrix into two M 1 =R m * k , M 2 =R k * n low-dimensional matrices. The two low-dimensional vectors are multiplied to obtain the similarity matrix of the original matrix, and the elements of the similarity matrix represent the user's preference for an app.…”