Predicting Next-use Mobile Apps Using App Semantic Representations

Abstract

Using the app usage history of a target user as a basis, this study proposes a novel method for predicting next-use mobile apps of the user that can assist the user in selecting an app from a list of installed apps. The proposed method is designed to train a next-use app prediction model using semantic representations of the usage histories of other users (source users) to deal with the user and app cold-start problems of an app prediction system in which training data from a target user beginning to use the system and training data related to newly installed or released apps are considered to be insufficient. We predict the usage of apps by a target user by leveraging the semantic similarities between the apps that are installed on the smartphones of the source users and the apps that are installed on the smartphone of a target user, permitting us to predict next-use apps regardless of the apps installed in the target user's smartphone. We evaluate our method using the actual app usage data collected from 100 participants over a period of approximately 70 days with 300,000 app usage histories.