Smartphone App Usage Prediction Using Points of Interest
This enables location-based app predictions for operating systems, network operators, and advertisers, but is incremental as it builds on transfer learning and POI data.
The paper tackles the problem of predicting smartphone app usage at a population level by analyzing geo-tagged data from over 6 million devices in Shanghai, achieving an 83.0% hitrate for top five apps and a 0.15 RMSE with 10% sparse data, outperforming existing methods by 25.7%.
In this paper we present the first population-level, city-scale analysis of application usage on smartphones. Using deep packet inspection at the network operator level, we obtained a geo-tagged dataset with more than 6 million unique devices that launched more than 10,000 unique applications across the city of Shanghai over one week. We develop a technique that leverages transfer learning to predict which applications are most popular and estimate the whole usage distribution based on the Point of Interest (POI) information of that particular location. We demonstrate that our technique has an 83.0% hitrate in successfully identifying the top five popular applications, and a 0.15 RMSE when estimating usage with just 10% sampled sparse data. It outperforms by about 25.7% over the existing state-of-the-art approaches. Our findings pave the way for predicting which apps are relevant to a user given their current location, and which applications are popular where. The implications of our findings are broad: it enables a range of systems to benefit from such timely predictions, including operating systems, network operators, appstores, advertisers, and service providers.