Application prediction method, application preloading method and application preloading apparatus

An application preloading method, performed by a processor executing instructions stored on a memory, wherein the method comprises:

• obtaining usage status of at least two applications running on a terminal at instant t, and usage status of the at least two applications running on the terminal corresponding to instants t−1 to t−n, wherein, n is a natural number greater than or equal to 2;
• inputting the usage status to a pre-trained application prediction model to obtain probabilities to start the at least two applications, wherein the application prediction model is generated by training a preset gated recurrent unit (GRU) neural network model from a plurality of association record groups of usage timing, and the plurality of association record groups of usage timing are obtained by grouping association record of usage timing of the at least two applications in a preset time period, wherein the association record of usage timing comprises a usage record of the at least two applications and a usage timing relationship of the at least two applications;
• and
• determining an application to be started corresponding to instant t+1 according to the probabilities to start the at least two applications, and preloading the application to be started.

An application prediction apparatus, comprising a processor and a memory storing instructions thereon, the processor when executing the instructions, being configured to:

• obtain a user behavior sample in the preset time period, wherein the user behavior sample comprises the association record of usage timing of the at least two applications;
• group the association record of usage timing to obtain the plurality of association record groups of usage timing;
• and
• train the preset GRU neural network model according to the plurality of association record groups of usage timing to generate the application prediction model.