Multimedia Data Recommendation Using Label Tree Similarity
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Solution Overview
Problem
Current data recommendation systems for multimedia content, such as videos and webpages, often recommend advertisements randomly, leading to reduced accuracy as they are not tailored to the specific content being viewed, resulting in unrelated recommendations.
Innovation Solution
A method and apparatus that acquire a label set representing content attributes of multimedia data, determine set similarity with a label tree, and recommend target data based on this similarity, ensuring that the recommended data matches the multimedia content more accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data recommendation is performed randomly without content matching, then the recommendation system is simple to implement, but the recommendation accuracy deteriorates
Solution Approach 1:
The system pre-acquires label sets for multimedia data and to-be-recommended data before the recommendation process. These label sets contain content attributes that are prepared in advance, enabling accurate matching when recommendations are generated without requiring complex real-time analysis
Solution Approach 2:
Label sets serve as intermediaries between multimedia data and recommendation data. Instead of directly comparing complex data structures, the system uses simplified label representations (content attributes) as mediators to calculate set similarity, reducing computational complexity while maintaining accuracy
2Measurement precision
If content-specific recommendation is implemented using label sets and similarity calculation, then recommendation accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system extracts essential content attributes from multimedia data and recommendation data into compact label sets. Instead of processing entire data structures, only the relevant label information is retained and used for similarity calculation, significantly reducing computational resources
Solution Approach 2:
The system transforms complex data representation into simplified label-based parameters. By changing from processing raw multimedia data and recommendation data directly to processing their label set representations, the computational complexity is reduced while preserving the essential matching information
Data Source
AI summary
A data recommendation method is described. A first label set corresponding to multimedia data can be acquired. At least one second label set each corresponding to one of at least one to-be-recommended data can be acquired. Each second label set can include at least one label each representing a content attribute of the respective to-be-recommended data. A set similarity between the first label set and each of the at least one second label set can be determined according to label positions in the label tree. Target recommendation data matched with the multimedia data can be determined from the to-be-recommended data set according to the set similarity between the first label set and each of the at least one second label set.