# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 1 Introduction  
Mobile Apps have blossomed in popularity and ubiquity in the recent decade. According to the statistic by _buildfire_ ([[https://buildfire.com/app-statistics/](https://buildfire.com/app-statistics/)]([https://buildfire.com/app-statistics/](https://buildfire.com/app-statistics/))), there are over 2.7 billion smartphone users across the world and 90% of mobile time is spent on Apps. The Apple App Store has 2.2 million Apps available for downloading and 2.8 million on the Google Play Store. With so many Apps out there, lots of data about application usage patterns can be collected from smartphones in physical spaces and user preferences can be extracted from these data to improve user experience. At the same time, system support that can improve our daily App interaction experience is poised to be widely beneficial. However, smartphone users install an average of 80-90 Apps per person on their device under investigation and use not more than 30 Apps each month even including newly installed Apps. Therefore, it has been widely studied which applications will be used and which do not need to be used. App usage prediction refers to the task of predicting the next App that will be used for a given user and at a given time [1]. An accurate prediction can not only improve the user experience but also can be used to optimize startup acceleration, energy consumption, bandwidth allocation, etc. For instance, smartphones can pre-load the possible Apps into the memory for reducing the startup time of Apps if we can predict the exact next App the user is the most likely to launch. The generalapproach is to transform the data of the physical world into the cyberspaces to build models. In recent years, researchers have used various methods to build models for prediction with information collected from mobile terminals and combined online and offline learning to improve prediction accuracy, such as taking advantage of temporal information [2, 3] or mining application usage patterns [4] to predict. However, there is apparent diversity of application-related features on smart devices. Furthermore, both the number of Apps users only use seldom even just once and the amount of data containing user portrait, temporal information, location information, mobile phone brand, and other related information are growing. Machine learning methods like LSTM (Long Short-Term Memory) do not perform very well in this complex context. Many previous studies have tackled the App prediction problem by means of network embedding, which can better mine the relationship between different types of applications and other information like time, location, etc. [PERSON] et al. [5] put forward extracting features and relationships from user-App bipartite. This bipartite not only describes the correlation between user and App but also expresses conjunctions of Apps used by a typical user or users who used the same application. However, limited information was exploited in the method because there is no contextual information. Previous endeavors have revealed that place and temporal information or the latest used App all benefit App prediction [6]. [PERSON] et al. [7] proposed one method named CAP, which constructs one network representing App usage records and contains different types of nodes and edges with all users' records. Then, build users' profiles for users' personalized predictions. However, CAP does not use attributed information, so it is not friendly with the prediction of new nodes. This paper proposes a framework named AHNEAP based on attributed network embedding for predicting the next App to be used for each user, by characterizing the context information before one specific App being launched. At present, we use data of the single user for experiments about our framework, but AHNEAP is also suitable for training multi-user data and predicting. Thus, data of each user will be uploaded to the cloud firstly.