A Smartphone User Activity Prediction Framework Utilizing Partial Repetitive and Landmark Behaviors

In this paper, we propose a general smartphone user activity prediction framework utilizing the general concept of partial repetitive behavior (instead of the stronger periodicity condition) for similarity scoring and the landmark behaviors (representative behaviors to identify groups of similar behavior vectors). Prediction of the next-day(s) behavior is based on a weighted sum of the most similar behavior vectors related to the landmark behavior of the next-day(s) behavior. These behavior vectors are selected based on the likely partial repetition of the next-day behavior and similarity in the eigen behavior feature space. Our proposed prediction algorithm allows one to categorically quantify the frequency of a target behavior, such as no behavior, normal behavior, and high frequency behavior, or other more refined categorization based on user preference. Extensive experiments are carried out using the Nokia Mobile Data Challenge (MDC) dataset to demonstrate the feasibility of our proposed approach and its generality using arbitrary call activity, voice call activity, short message activity, media consumption, and apps usage data types.