DIGITAL CONTENT CLASSIFICATION AND RECOMMENDATION BASED UPON ARTIFICIAL INTELLIGENCE REINFORCEMENT LEARNING

摘要

Methods and apparatuses are described for digital content classification and recommendation based upon reinforcement learning. A server converts unstructured text corresponding to each digital content item into a content item feature set. The server generates a user context vector associated with a plurality of users. The server trains a linear multi-armed bandit (MAB) classification model based upon the user context vectors and historical user content recommendation information. The server receives a new user context vector associated with a new user. The server executes the MAB model using the new user context vector as input to generate content interaction prediction scores. The server selects the content interaction prediction scores above a predetermined threshold and identifies the associated digital content item. The server presents the identified digital content items on a client device and receives a response. The server updates linear UCB coefficient vectors of the MAB model based upon the response.