Relationship Learning Model for Visual Content Recommendation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional content recommendation methods rely on simple text-based classification, which is inefficient in accurately identifying users' preferred styles and product combinations, leading to low accuracy and difficulty in recommending suitable interior and furniture products.
Innovation Solution
A device and method utilizing a relationship learning model based on user rendering history to index and recommend visually preferred content products or combinations, applying user information to a pre-constructed relationship learning model to provide accurate recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If simple text-based classification analysis is used for content recommendation, then the system is easy to implement and operate, but the recommendation accuracy is low and cannot accurately identify user preferred styles
Solution Approach 1:
The system performs preliminary actions by collecting and storing rendering history data in advance, building a comprehensive database of user visual preferences before recommendation is needed. This pre-collection of visual interaction data enables accurate style identification when recommendations are generated, resolving the contradiction between accuracy and complexity.
Solution Approach 2:
The patent transitions from one-dimensional text-based classification to multi-dimensional visual feature analysis by examining rendering history across multiple attributes (visual elements, composition, color schemes, user interactions). This dimensional expansion enables accurate style identification while maintaining system manageability through structured analysis frameworks.
2Measurement precision
If conventional text-based classification is used, then the processing speed is fast and simple, but the ability to identify user preferred styles and product combinations is insufficient
Solution Approach 1:
The system performs preliminary action by pre-processing and structuring rendering history data into organized datasets before recommendation queries. Visual features are extracted and categorized in advance, creating ready-to-analyze data structures that enable rapid accurate style identification without time loss during actual recommendation operations.
Solution Approach 2:
The patent replaces mechanical text-based classification systems with visual feature analysis mechanisms that process rendering history data through automated image recognition and pattern matching algorithms. This substitution enables simultaneous processing of multiple visual attributes, achieving high style identification accuracy without proportional increases in processing time.
3Measurement precision
If visual feature information and rendering history are used for recommendation, then recommendation accuracy is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The system applies segmentation by dividing visual feature analysis into distinct modular components: rendering history collection, visual feature extraction, pattern recognition, and recommendation generation. Each module processes specific aspects of the data independently, reducing overall system complexity while maintaining high recommendation accuracy through coordinated modular operations.
Solution Approach 2:
The patent introduces intermediary data structures that bridge raw rendering history and final recommendations. Visual feature vectors and style profiles serve as intermediary representations that simplify complex visual data into structured formats, enabling accurate recommendations without requiring the entire system to handle full complexity of raw visual information simultaneously.
Data Source
AI summary
A method of operating a recommendation information providing device according to an embodiment of the present disclosure may include acquiring first user information; applying the first user information to a relationship learning model based on a rendering history corresponding to content; and providing content recommendation information corresponding to the first user information using the output information of the learning model.