## 2 Related Work  
### Mobile App Start-Up Prediction  
Mobile App start-up prediction is a kind of prediction recommendation issue. Its purpose is to predict the mobile Apps that users are most likely to use in the next period, so as to realize the preloading of Apps and save the start-up time of Apps for users. The information that the mobile App start-up prediction method relies on for modeling is mainly related to the usage patterns and the context of the user's App. App usage patterns refer to the App's usage cycle, usage sequence, etc. The temporal-sequence correlation can be studied from two perspectives: the usage temporal sequence of all installed Apps, and each App's separate usage temporal sequence. The usage temporal sequence of all installed Apps refers to the start-up sequence of Apps in a period of time, whereas each App's separate usage temporal sequence refers to the start-up sequence of the App in that period of time. By analyzing the temporal sequence of each App separately, we can determine the time rule for the usage of the App, and then analyze the temporal sequence of all Apps, and find the periodicity of user behavior [5]. For example,Reference [5] divides the usage temporal sequence of each mobile App according to the length of the time slot, so as to mine the periodicity and recent behavior of users using mobile Apps. Reference [6] uses the variant of App's usage temporal sequence to perform hierarchical clustering on the number of startups of each mobile App in a unit of time to determine a variety of user behaviors. The separate analysis of each App's usage temporal sequence attaches great importance to studying the time rule of each App, but ignores the correlation between Apps. For example, after paying with Alipay, the purchaser usually opens an SMS to check the balance of their bank card. To a certain extent, the startup sequence of different Apps has a sequence association, which may be caused by the user's own habits, or by the automatic jump between Apps. Therefore, some studies predict the next App the user is most likely to launch based on the usage sequence of all installed Apps. Reference [7] exploits the ideas presented in word2 vec to model the App's temporal sequence documents and uses a Gaussian-based method to identify the context of each App action to extract session features. According to the principle of Tree Augmented Naive Bayes (TAN) algorithm, the prediction model is established based on the basic features of spatiotemporal context (such as Time, Latitude, Longitude, Charging Cable, etc. ); Reference [8] uses a Bayesian network to draw the relationship between the App and the last used App, time, location, and the user profile (such as whether the mobile phone mode is in vibration or mute), and calculates the probability of each App according to the network. Context-based mobile App start-up prediction can often achieve higher accuracy after fusing multi-party contextual information. Some studies are based on probabilistic graphic models to realize the context-based mobile App start-up prediction, such as the conditional probability model [9], Markov model [10; 11], and Bayesian network [7; 8; 12]. [PERSON] et al. proposed an effective Bayesian network-based personalized classification method, using the joint features from the basic contextual information and the App session-related contextual information, such as the context change when an App is started, to solve the App prediction problem [7]. Reference [12] utilizes sensor data to extract contextual information, and uses a Bayesian model to predict. In recent years, researchers have collected local App usage records of mobile terminals and exploited machine learning methods to establish prediction models, such as inferring Apps from temporal profiles [6; 13] and mining App usage patterns for prediction [14], etc., which can improve prediction accuracy. [PERSON] et al.