Dynamic Image Ranking for Search Content Relevance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current search engines lack efficient methods to rank images effectively for matching with content items, resulting in unattractive search results.
Innovation Solution
An image ranking mechanism is implemented that calculates feature scores for image attributes and metadata, determines matching type scores based on search queries, and calculates matching quality scores using algorithms trained on historical data to rank images accurately for content items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional search engines return content items without image matching, then the search operation is simple and fast, but the search results are unattractive and less relevant to users
Solution Approach 1:
The image ranking mechanism is segmented into multiple independent scoring components: feature scores (image attributes), matching type scores (search query alignment), and matching quality scores (historical performance). Each component operates independently and contributes to the final composite score, allowing the system to manage complexity through modular design while improving result relevance.
Solution Approach 2:
The patent introduces an intermediary image ranking mechanism that sits between the search query processing and the final search results presentation. This intermediary layer evaluates multiple images against content items using sophisticated scoring algorithms, selecting the best matches without requiring fundamental changes to the core search engine architecture, thus improving relevance while controlling complexity.
2Reliability
If an image ranking mechanism is implemented to match images with content items, then the attractiveness and relevance of search results is improved, but the computational complexity and processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing feature scores for image attributes and metadata before the actual search execution. When a search query is received, the matching type scores and final ranking are computed based on these pre-prepared features, significantly reducing the computational burden and processing time during the search operation itself.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the weightings of different scoring components (feature scores, matching type scores, matching quality scores) based on the specific search context and query type. This allows the system to optimize processing time by emphasizing certain parameters over others depending on the search scenario, balancing matching quality with computational efficiency.
3Measurement precision
If multiple scoring algorithms are used to rank images, then the accuracy of image selection is improved, but the device complexity and computational resources required increase
Solution Approach 1:
The scoring system is segmented into three distinct algorithms: feature score calculation (image attribute evaluation), matching type score calculation (search query alignment), and matching quality score calculation (historical performance). Each algorithm operates independently with its own logic and data requirements, making the overall complex system manageable through clear separation of concerns and independent optimization of each component.
Solution Approach 2:
The patent creates a universal scoring framework where the same multi-component scoring mechanism is applied across different search types, image categories, and content items. The feature scores, matching type scores, and matching quality scores serve multiple functions: evaluating image quality, assessing query relevance, and predicting user preference, thereby reducing the need for separate specialized algorithms for different scenarios.
Data Source
AI summary
According to one embodiment, images are identified to be matched with a content item that is identified in response to a search query. For each of the images, a matching type score is calculated based on a first set of one or more keywords associated with the search query and a second set of one or more keywords associated with the image. A matching quality score is calculated for the image based on the matching type score, the first set of keywords, and the second set of keywords. The images are ranked based on their respective matching quality scores. One of the images with a matching quality score higher than a predetermined threshold is selected, where the selected image is to be associated with the content item.