User Interface for Optimizing Digital Page Content
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Solution Overview
Problem
Digital pages of users on online services often lack relevant data, leading to diminished accuracy, relevancy, and completeness of search results, and users face difficulties in efficiently adding necessary data to their pages, resulting in suboptimal performance of the computer system.
Innovation Solution
A user interface system that identifies job postings relevant to a user's interests, extracts key phrases, and suggests their placement on the user's page using a placement classifier, allowing users to conveniently add these phrases to enhance their page content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually add relevant data to their digital pages, then the accuracy and relevancy of search results improve, but the time and effort required increases significantly
Solution Approach 1:
The system automatically analyzes the user's profile data, job applications, and activity history to generate optimized page content without requiring manual user input. The computer system performs self-service by autonomously identifying relevant keywords, skills, and achievements from available data sources and formatting them into optimized page sections.
Solution Approach 2:
The system performs preliminary analysis of user data and pre-generates optimized content recommendations before the user needs them. By proactively analyzing profile information, job applications, and activity patterns in advance, the system prepares optimized page content that can be immediately applied when needed, eliminating the time-consuming manual data addition process.
2Loss of information
If users add more relevant data to their pages, then the completeness of search results improves, but the complexity of page management increases
Solution Approach 1:
The system divides the page optimization task into distinct segments: analyzing profile data, extracting job application information, identifying activity patterns, generating keyword recommendations, and formatting content. By segmenting the complex optimization process into manageable components, the system reduces the perceived complexity for users while ensuring comprehensive data utilization.
Solution Approach 2:
The system acts as an intermediary between the user's raw data and the final optimized page content. It mediates by automatically processing profile information, job applications, and activity history through analysis algorithms, then presenting refined recommendations that bridge the gap between raw data and optimized output, simplifying page management.
3Measurement precision
If the system provides detailed recommendations for page optimization, then the relevancy of user profiles improves, but the computational resources consumed increase
Solution Approach 1:
The system applies partial optimization by focusing computational resources on the most impactful page sections and keywords rather than uniformly processing all content. It identifies and prioritizes critical profile elements that have the greatest impact on search relevancy, applying detailed analysis only where needed while using more efficient processing for less critical sections.
Solution Approach 2:
The system dynamically adjusts analysis parameters based on user context, data availability, and search patterns. It modifies the depth and intensity of computational analysis according to specific profile characteristics and search query types, optimizing the balance between profile relevancy improvement and computational resource consumption by adapting processing intensity to actual needs.
Data Source
AI summary
Techniques for improving the accuracy, relevancy, and efficiency of a computer system of an online service by providing a user interface to optimize a digital page of a user on the online service are disclosed herein. In some embodiments, a computer system identifies job postings published on an online service as corresponding to a type of job based on feature data of each one of the job postings, extracts phrases from the identified job postings based on a corresponding relevancy measurement and a corresponding diversity measurement for each one of the phrases, determines a corresponding section of a page of a user to suggest for placement of the extracted phrase using a placement classifier for each one of the extracted phrases, and generates a corresponding recommendation for the page based on the extracted phrase and the determined section of the extracted phrase for each one of the phrases.