Web Browser Tab Grouping via Semantic Clustering
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Solution Overview
Problem
Conventional web page grouping methods in browsers require multiple clicks to access grouped web pages, leading to poor user experience and low grouping accuracy, as users need to manually set URLs and navigate through unorganized tab bars to find previously opened pages.
Innovation Solution
A method and apparatus that extract titles from web page labels, calculate semantic distances, cluster them based on these distances, and arrange label groups sequentially in the tab bar, allowing web page labels to be grouped automatically without manual URL setting, enabling single-click access and improved grouping accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If web page labels are grouped by manually setting URLs and using group labels, then web pages can be categorized, but users need to click multiple times to access grouped web pages and the operation process becomes complicated
Solution Approach 1:
The system automatically extracts URLs from web page labels and performs clustering based on URL similarity without requiring manual group creation or URL setting by users. The grouping is self-organized through the clustering algorithm, eliminating manual configuration steps.
Solution Approach 2:
Instead of requiring users to manually create groups and assign URLs (traditional approach), the system inverts the process by automatically analyzing URL patterns and organizing labels into groups based on similarity. The inversion transforms a manual categorization task into an automatic clustering process.
2Productivity
If web page labels are arranged in chronological order in the tab bar, then new pages are automatically added, but finding previously opened pages requires searching through many labels
Solution Approach 1:
The system segments the chronological sequence of web page labels by clustering them into multiple groups based on URL similarity. This segmentation creates logical categories (e.g., shopping, news, work) within the chronological flow, allowing users to mentally organize and locate pages more efficiently without physical reordering.
Solution Approach 2:
The system applies different visual indicators (such as background colors or icons) to web page labels belonging to different clusters. This visual differentiation helps users quickly identify and locate pages from specific categories without having to read through all label titles, reducing search time.
3Measurement precision
If conventional grouping methods are used with group labels, then web pages can be categorized, but the grouping accuracy is low and requires manual URL setting
Solution Approach 1:
The system replaces the manual mechanical process of URL setting and group configuration with an automated computational clustering algorithm. The algorithm analyzes URL patterns, extracts features, and automatically groups labels based on similarity metrics, eliminating manual configuration while improving accuracy.
Solution Approach 2:
The clustering algorithm continuously refines group assignments by comparing URL patterns and adjusting cluster formations based on similarity measurements. This feedback mechanism ensures high grouping accuracy by automatically adapting to the actual content patterns of the web pages rather than relying on manual categorization rules.
Data Source
AI summary
Disclosed in the embodiments of the disclosure are methods and apparatuses for grouping web page labels. The method comprises: extracting titles of a plurality of web page labels in a tab bar of a browser; calculating semantic distances between the extracted plurality of titles; clustering web page labels corresponding to the plurality of titles based on the semantic distances between the plurality of titles; obtaining at least one label group based on the clustering, the one label group comprising at least one web page label; and sequentially arranging the at least one label group in the tab bar, the web page labels belonging to the same label group being successively arranged in the tab bar. After grouping, the web page labels are still presented in the format of web page labels in the tab bar and a web page label can be selected with a single click. Moreover, web page labels belonging to the same label group are successively arranged together in the tab bar, such that the web page labels in the tab bar are arranged and ordered and a user can find a needed web page label quickly through the tab bar, thus improving user experience.