Cohort Modeling Based App Category Usage Prediction

Abstract

Smartphones utilize context signals, such as time and location, to predict users' app usage tailored to individual users. To be effective, such personalization relies on access to sufficient information about each user's behavioral habits. For new users, the behavior information may be sparse or non-existent. To handle these cases, app category usage prediction approaches can employ signals from users who are similar along one or more dimensions, i.e., those in the same cohort. In this paper, we describe a characterization and evaluation of the use of such cohort modeling to enhance app category usage prediction. We experiment with pre-defined cohorts from three taxonomies - demographics, psychographics, and behavioral patterns - independently and in combination. We also evaluate various approaches to assign users into the corresponding cohorts. We show, through extensive experiments with large-scale mobile app usage logs from a mobile advertising company, that leveraging cohort behavior can yield significant prediction performance gains than when using the personalized signals at the individual prediction level. In addition, compared to the personalized model, the cohort-based approach can significantly alleviate the cold-start problem, achieving strong predictive performance even with limited amount of user interactions.