Browser Relevancy Sorting via Weighted History Analysis
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Solution Overview
Problem
Current Web browsers require multiple user actions to navigate to relevant destinations due to inefficient auto-complete features, leading to diminished productivity and a frustrating browsing experience.
Innovation Solution
Implementing a relevancy algorithm in the Web browser that assigns weights to various parameters such as URL matches, titles, frequency of visits, and user interactions to provide dynamic and relevant suggestions in the address bar drop-down menu.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If uniform resource locator (URL) prefix string matching is used to provide auto-complete suggestions, then suggestions are provided based on user input, but multiple user actions are required to search through suggestions and find relevant destinations
Solution Approach 1:
The system automatically calculates relevancy scores and sorts suggestions without requiring user intervention. The browser autonomously analyzes browsing history, URL matches, titles, and visit frequency to rank suggestions, eliminating the need for manual searching through unsorted lists.
Solution Approach 2:
The patent introduces multiple parameters (URL match score, title match score, visit frequency, time since last visit) to evaluate and rank suggestions. By changing from simple prefix matching to a multi-parameter relevancy algorithm, the system prioritizes the most relevant destinations at the top of the suggestion list.
2Productivity
If multiple user actions are required to search through auto-complete suggestions, then comprehensive suggestions can be provided, but productivity and browsing efficiency are diminished
Solution Approach 1:
The system pre-calculates and pre-sorts suggestions based on relevancy algorithms before the user needs them. Browsing history and suggestion relevance are analyzed in advance, so when the user types in the address bar, the most relevant suggestions are already positioned at the top, requiring minimal user interaction.
Solution Approach 2:
The system uses user browsing behavior as feedback to continuously improve suggestion relevancy. By analyzing which suggestions users actually click on and their browsing patterns, the algorithm refines its ranking, making future suggestions even more accurate and reducing the number of actions needed.
3Measurement precision
If simple URL prefix matching is used, then the auto-complete feature is easy to implement, but relevant suggestions may not be provided accurately
Solution Approach 1:
The patent divides the relevancy calculation into distinct components: URL match scoring, title match scoring, visit frequency analysis, and time-based decay factors. Each component is calculated separately and then combined, making the complex algorithm more manageable and implementable through modular processing steps.
Data Source
AI summary
Various embodiments provide a Web browser that employs a relevancy algorithm to make an educated guess as to the likelihood of a user's intended destination when the user begins to enter text into a browser's address bar. In one or more embodiments, the relevancy algorithm employs various parameters and assigns weights to the parameters to arrive at a collection of suggestions to provide to the user. By using various rules, associated weightings, and the relevancy algorithm, relevant suggestions can be provided to a user to facilitate their navigation activities.