Dynamic Category Classification for Social Network Image Browsing
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Solution Overview
Problem
In social network services, the browsability of uploaded images is degraded when a large number of users contribute a large number of works, as the existing techniques for categorization are not user-centric and often rely on simple keyword correlations or work quantity, making it difficult for users to efficiently access desired content.
Innovation Solution
A network system that classifies image data into categories based on user interest by using a classification unit to group works with high browse request counts into separate categories, allowing for dynamic adjustment of category numbers based on popularity, and includes a display control unit to showcase these categories on user terminals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If works are classified into multiple categories to improve browsability, then the number of categories increases, but the complexity of category management increases
Solution Approach 1:
The patent implements dynamic category management where the number and structure of categories automatically adjust based on browse request counts. The category division unit dynamically creates new categories or merges existing ones based on real-time user browsing behavior, eliminating the need for static manual category configuration and reducing management complexity while maintaining good browsability.
Solution Approach 2:
The count unit continuously monitors browse request counts and provides feedback to the category division unit. This feedback mechanism enables the system to automatically optimize category structure based on actual user behavior patterns, resolving the contradiction by allowing the system to adaptively manage category complexity according to actual browsing needs rather than requiring predetermined complex category structures.
2Ease of operation
If works are classified into more categories to improve accessibility, then the number of categories increases, but the difficulty of category classification increases
Solution Approach 1:
The system performs automatic category classification without requiring manual intervention. The category division unit automatically analyzes browse request counts and performs classification based on predefined rules, eliminating the need for manual category assignment and reducing classification difficulty while improving accessibility through data-driven automatic categorization.
3Productivity
If the number of works in each category is limited to improve display efficiency, then browsability improves, but the loss of information increases
Solution Approach 1:
The patent segments works into multiple categories based on browse request counts, allowing efficient display within each category while ensuring comprehensive visibility across all categories. The category division unit creates multiple categorized groups that collectively contain all works, so no work is lost while maintaining display efficiency through organized segmentation.
Data Source
AI summary
There is provided a network system in which image data items are uploaded from a plurality of user terminals to a server and images are opened to public among the users. The system includes a category division unit configured to divide the works classified into the categories into a first group of works with each of which the counted browse request number of times is greater than or equal to a predetermined number, and a second group of works other than the works in the first group, and classify one of the first and second groups of the divided works as another category different from the categories.