CAP

Abstract

Context-aware mobile application (App) usage prediction benefits a variety of applications such as precise bandwidth allocation, App launch acceleration, etc. Prior works have explored this topic through individual data profiles and contextual information. However, it is still a challenging problem because of the following three aspects: i. App usage behavior is usually influenced by multiple factors, especially temporal and spatial factors. ii. It is difficult to describe individuals' preferences, which are usually time-variant. iii. A single user's data is sparse on the spatial domain and only covers a limited number of locations. Prediction becomes more difficult when the user appears at a new location. This paper presents CAP, a context-aware App usage prediction algorithm that takes both contextual information (location & time) and attribution (App with type information) into consideration. We find that the relationships between App-location, App-time, and App-App type are essential to prediction and propose a heterogeneous graph embedding algorithm to map them into the common comparable latent space. In addition, we create a user profile for each user with App usage and trajectory history to describe the individual dynamic preference for personalized prediction. We evaluate the performance of our proposed CAP with two large-scale real-world datasets. Extensive evaluations demonstrate that CAP achieves 30% higher accuracy than a state-of-the-art method Personalized Ranking Metric Embedding (PRME) in terms of Accuracy@5. In terms of mean reciprocal rank (MRR), CAP achieves 1.5× higher than the straightforward baseline Sta and 2× higher than PRME. Our investigation enables a range of applications to benefit from such timely predictions, including network operators, service providers, and etc.