Due to a huge amount of mobile applications (abbreviated as Apps), for Apps providers, the usage preferences of Apps are important in recommending Apps, downloading Apps and promoting Apps.We predict and quantize users' dynamic preferences by exploring their usage traces of Apps. To address the dynamic preference prediction problem, we propose Mode-based Prediction (abbreviated as MBP) and Reference-based Prediction (abbreviated as RBP) algorithms. Both MBP and RBP consist of two phases: the trend detection phase and the change estimation phase. In the trend detection phase, both algorithms determine whether the preference of an App is increasing or decreasing. Then, in the change estimation phase, the amount of preference change is calculated. In particular, MBP adopts users' current usage mode (active or inactive), and then estimates the amount of change via our proposed utility model. On the other hand, RBP calculates an expected number of usage as a reference, and then builds a probabilistic model to estimate the change of preference by comparing the real usage and the reference. We conduct comprehensive experiments using two App usage traces and one music listening log, the Last.fm dataset, to validate our proposed algorithms. The experimental results show that both MBP and RBP outperform the usage-based method that is based solely on the number of usages.