Automated Recommendation Information Extraction System
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Solution Overview
Problem
Existing multimedia content creation processes are inefficient and of low quality due to manual browsing and extraction of object feature information, limiting the data volume and quality of recommendation information.
Innovation Solution
A method and apparatus for determining recommendation information that includes displaying an information recommendation page with object feature information based on multimedia data meeting preset conditions, allowing selection and confirmation of high-quality features for efficient recommendation generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual browsing and extraction of multimedia resources is used to obtain object feature information, then the process allows human judgment and selection, but the efficiency is low and the amount of data processed is limited
Solution Approach 1:
The patent replaces the manual mechanical process of browsing and extracting feature information with an automated computer-based system. The system automatically retrieves multimedia resources, extracts feature information using algorithms, and generates recommendation information without human intervention, thereby dramatically improving productivity and eliminating time loss associated with manual operations.
Solution Approach 2:
The system enables self-service by automatically performing the entire workflow of retrieving multimedia resources, extracting object feature information, and generating recommendations. The automated system serves itself by continuously processing data and providing recommendation information without requiring manual initiation or intervention for each extraction task.
2Manufacturing precision
If manual extraction of object feature information is performed, then the creator can select relevant features, but the quality of information is not high due to limited data volume
Solution Approach 1:
The system performs multiple functions simultaneously: it retrieves multimedia resources from various sources, extracts diverse feature information (object identification, attributes, usage scenarios), and generates comprehensive recommendation information. This multi-functional approach processes a much larger quantity of data than manual methods while maintaining high quality through automated analysis of multiple data sources.
Solution Approach 2:
The automated computer-based extraction system processes significantly larger volumes of data compared to manual extraction. The system can analyze numerous multimedia resources simultaneously, extracting comprehensive feature information including object identification, attributes, and usage scenarios, thereby improving both the quantity and quality of processed information.
3Productivity
If automated processing of multimedia information is implemented, then efficiency and data volume are improved, but the complexity of the system increases
Solution Approach 1:
The system segments the complex task of feature extraction into distinct functional modules: a retrieval module that obtains multimedia resources, an extraction module that identifies object feature information, and a generation module that creates recommendation information. This segmentation manages system complexity by organizing automated processing into manageable, specialized components that work together efficiently.
Solution Approach 2:
The system introduces an intermediary processing layer that automatically extracts feature information from multimedia resources and transforms it into structured recommendation information. This intermediary module bridges the gap between raw multimedia data and usable recommendation outputs, managing the complexity of automated processing through a dedicated transformation layer.
Data Source
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AI summary
Embodiments of the present disclosure provide a method and apparatus for determining recommendation information, an electronic device, and a computer readable medium. The method comprises: in response to an object selection operation, determining an object to be recommended, and displaying an information recommendation page corresponding to said object, wherein the information recommendation page comprises a plurality of pieces of object feature information of said object, and the object feature information of said object is determined according to multimedia information satisfying a preset condition and corresponding to said object; and in response to a determination operation for at least one piece of object feature information of said object, using the at least one piece of object feature information as recommendation information of said object.