Digital avatar recommendation method and recommendation system

A digital avatar recommendation system, comprising one or more storage devices and one or more processors, the one or more storage devices, individually or collectively, having executable instructions stored thereon, the executable instructions when executed by the one or more processors, enabling the one or more processors to, individually or collectively, implement a state acquisition module, a computer-simulated digital avatar, an agent embedded in the digital avatar, wherein:

• the state acquisition module is configured to obtain current state data, the state data including user information of a target user, scenario information of a current scenario, and history information of an interaction between the target user and the digital avatar;
• the agent in the digital avatar is configured to map the current state data to a target action in a candidate action set based on a current policy obtained through reinforcement learning, a candidate action in the candidate action set corresponding to a to-be-recommended content category, and the target action corresponding to a target content category;
• and
• the digital avatar is configured to perform a target interaction with the target user, wherein the target interaction is used to recommend the target content category.

A storage medium having computer executable instruction stored thereon, the computer executable instructions, when executed by one or more processors, enabling the one or more processors to, individually or collectively, implement a state acquisition module, a computer-simulated digital avatar, an agent embedded in the digital avatar, wherein:

• the state acquisition module is configured to obtain current state data, the state data including user information of a target user, scenario information of a current scenario, and history information of an interaction between the target user and the digital avatar;
• the agent in the digital avatar is configured to map the current state data to a target action in a candidate action set based on a current policy obtained through reinforcement learning, a candidate action in the candidate action set corresponding to a to-be-recommended content category, and the target action corresponding to a target content category;
• and
• the digital avatar is configured to perform a target interaction with the target user, wherein the target interaction is used to recommend the target content category.