Patent ID: 8750602
Filing Date: 2014-06-10
Classification: G06F,G06K,G06Q

Abstract:
1. A personalized advertisement push method based on user interest learning, wherein the method comprises: obtaining a plurality of user interest models through multitask sorting learning; extracting an object of interest in a video according to the user interest models; and extracting multiple visual features of the object of interest, and retrieving related advertising information in an advertisement database according to the multiple visual features; wherein the obtaining a plurality of user interest models through multitask sorting learning comprises: obtaining various scenes in training data, and extracting bottom-layer visual features of each macroblock in each scene; and performing classification on users and classification on scenes according to the bottom-layer visual features through an algorithm of multitask sorting learning, and establishing the interest models for each classification of users on each classification of scenes; wherein the performing classification on users and classification on scenes according to the bottom-layer visual features through an algorithm of multitask sorting learning, and the establishing the interest models for each classification of users on each classification of scenes comprises: randomly grouping the users and the scenes into multiple classifications respectively, and initializing an interest model for each classification of users on each classification of scenes; establishing a loss function on a training set as an optimization target by using an initialized interest model; minimizing the loss function through an optimization algorithm, and updating a parameter value of each interest model, and optimizing classifications of the users and the scenes; and obtaining final classifications of users and scenes, and a plurality of user interest models.