AI Reply Generation for Interactive Media Content
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Solution Overview
Problem
Existing methods for replying to interactive media content, such as comments and barrages, require users to actively input their responses, leading to low reply efficiency due to the manual effort involved.
Innovation Solution
A method and apparatus that utilize encoding processing and style recognition to generate reply content by determining the style category of the interactive content and release party information, allowing for automatic prediction and generation of replies based on vectorized representations and style category vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually input reply content to respond to interactive content, then the reply can be customized and accurate to user intent, but the reply efficiency is low due to the active input requirement
Solution Approach 1:
The system performs self-service by automatically generating reply content without requiring user input. The AI model analyzes the interactive content and autonomously produces appropriate replies, eliminating the need for users to manually type responses while maintaining reply quality and efficiency
2Measurement precision
If the system automatically generates reply content without style recognition, then the reply generation process is simpler, but the reply accuracy and relevance to user intent are reduced
Solution Approach 1:
The system performs preliminary action by conducting style recognition and intent analysis before generating the reply content. This preliminary processing identifies the user's intent and preferred communication style, which then guides the accurate generation of relevant and appropriate replies
Solution Approach 2:
The system incorporates feedback mechanisms where the generated reply is evaluated against the original interactive content and user profile. This feedback loop ensures the reply maintains high accuracy and relevance, allowing the system to continuously improve its performance
Data Source
AI summary
A reply content processing method including obtaining to-be-replied interactive content for media content, performing encoding processing on description content of the media content and the to-be-replied interactive content to obtain a vectorized representation of each word in the description content and the to-be-replied interactive content, and performing style recognition based on the vectorized representation of each word in the description content and the to-be-replied interactive content to obtain a first style category set, performing style recognition based on release party information of the to-be-replied interactive content to obtain a second style category set, determining a third style category set to which the to-be-replied interactive content belongs, determining a style category vector corresponding to each style category in the third style category set, and performing reply word prediction based on the description content, the to-be-replied interactive content, and the style category vector to generate reply content corresponding to each style category.