Keyword-Image Mapping Table for Search Result Relevance
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Solution Overview
Problem
Conventional search engines struggle to effectively match and integrate images with search results, leading to unattractive and unengaging content presentations, as they lack efficient mechanisms for identifying and ranking relevant images based on search queries.
Innovation Solution
A system is implemented that uses query-image matching rules to map predetermined keywords to image identifiers, with a process involving preliminary, expanded, and filtered matching tables to select and rank images based on semantic similarity and metadata analysis, ensuring images are relevant and engagingly integrated with search content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional search engines return content items as is without modification, then the search process is simple and fast, but the search results are unattractive and unengaging
Solution Approach 1:
The system performs preliminary actions by pre-processing search queries to extract keywords before content retrieval, and pre-processing content items to extract associated keywords. This preliminary keyword extraction enables efficient matching with image keywords without adding complexity to the user-facing search process.
Solution Approach 2:
The patent introduces keywords as an intermediary element that mediates between search queries and content items, and between content items and images. By matching content item keywords with image keywords, the system seamlessly integrates relevant images with search results without complicating the overall search operation.
2Difficulty of detecting and measuring
If the system integrates image matching with search results, then search result attractiveness is improved, but the system complexity increases
Solution Approach 1:
The system segments the image matching process into distinct modular components: query processing module for extracting search keywords, content processing module for extracting content keywords, keyword matching module for comparing keywords, and image selection module for choosing relevant images. This segmentation reduces system complexity by making each component independent and manageable.
Solution Approach 2:
The keyword-based matching mechanism serves multiple functions: it matches search queries with content items, matches content items with images, and can be applied to different types of content and images. This universal approach avoids the need for separate specialized systems for different matching tasks.
3Measurement precision
If the system uses comprehensive keyword matching to identify relevant images, then image relevance is improved, but the processing time increases
Solution Approach 1:
The system extracts only the most relevant keywords from search queries and content items rather than analyzing all text. It then performs partial matching by comparing these extracted keywords with image keywords, achieving sufficient image relevance without the computational overhead of comprehensive analysis of all content.
Data Source
AI summary
According to one embodiment, a first keyword-to-image (keyword/image) mapping table is provided. The first keyword mapping table includes a number of entries, each entry mapping a keyword to one or more image identifiers (IDs) identifying one or more images. For each of the keywords of the first keyword/image mapping table, an analysis is performed on the keyword to determine one or more related keywords that are related to the keyword. One or more additional entries corresponding to the one or more related keywords are generated to be incorporated into the first keyword/image mapping table to generate a second keyword/image mapping table. The second keyword/image mapping table is utilized to associated a particular image to a particular content item related to a particular keyword.