The invention discloses an APP interface
visual communication adaptive optimization method and
system based on
user satisfaction, and the method comprises the steps: constructing a visual element-satisfaction association prediction model, taking historical satisfaction
score, operation feedback and scene
feature data as a training basis, and carrying out the training through a
gradient boosting tree
algorithm; collecting real-time scene features and initial interaction data of a target user, and generating candidate visual element parameters and prediction scores; constructing an instant
preference vector, and screening a to-be-verified visual scheme in combination with
cosine similarity; a local
gray level display scheme is adopted, and multi-
modal feedback is collected; constructing an evaluation matrix, and calculating a real-time satisfaction comprehensive
score by using an
analytic hierarchy process; determining a scheme and incrementally updating the model if the standard is reached, and regenerating parameters if the standard is not reached; the
system comprises six components such as a model iteration engine and a real-
time data collector, and dynamic
adaptation is achieved cooperatively. According to the method, user preferences and scenes can be accurately matched, the
visual experience and the operation efficiency are improved,
continuous optimization can be achieved through model iteration, and the method is suitable for various APPs needing personalized interfaces.