Automated Content Suggestion via Textual Analysis
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Solution Overview
Problem
Web content management systems face inefficiencies in allowing authors to identify and add relevant content items to digital pages, as authors must manually search through vast repositories, making the process time-consuming and tedious.
Innovation Solution
Implementing a method that analyzes the textual information of a digital page using TF-IDF analysis to generate queries for a search engine, which identifies and suggests relevant content items from the repository, allowing authors to quickly add relevant content to their pages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If authors manually search through content repositories to find relevant content items, then they can identify and add appropriate content to digital pages, but the process becomes time-consuming and tedious
Solution Approach 1:
The system enables self-service by automatically analyzing the digital page content, extracting keywords, and identifying relevant content items from the repository without requiring manual author intervention. The web content management system performs the search and suggestion functions autonomously, freeing authors from time-consuming manual browsing while maintaining high relevance accuracy through automated text analysis and search algorithms.
2Quantity of substance
If the content repository contains a vast number of content items, then the system provides comprehensive content options for authors, but it increases the difficulty and time required to locate specific relevant items
Solution Approach 1:
The system introduces an intermediary mechanism that acts as a bridge between the vast content repository and the author's needs. The automated analysis component extracts keywords from the digital page, and the search system uses these keywords to filter and retrieve relevant content items from the large repository. This intermediary process transforms the overwhelming task of browsing vast repositories into a focused, automated search that maintains comprehensive content availability while dramatically improving ease of operation.
3Reliability
If authors review and evaluate each content item individually, then they can ensure high quality and relevance of selected content, but the overall content selection process becomes extremely time-consuming
Solution Approach 1:
The system applies preliminary action by pre-analyzing the digital page content and pre-identifying relevant content items before the author begins the selection process. The automated keyword extraction and search functions perform the initial filtering and ranking of content items based on their relevance to the page subject matter. This preliminary action maintains reliability by ensuring high-quality, relevant suggestions are presented to authors, while dramatically improving productivity by eliminating the need for authors to manually evaluate each item's relevance.
Data Source
AI summary
Methods for automatically suggesting content items to an author of a digital page so that they can be added to the page. One embodiment comprises a method that includes opening a digital page in a digital page editor and launching a component browser that enables the viewing of a set of digital content items stored in a content repository. Textual information is extracted from the digital page and the digital assets contained in the page. This information is analyzed to identify words indicative of the page's subject matter. A query is generated based on the identified words and submitted to a search engine, which searches the repository for items related to the subject matter of the page. The content items identified by the search results are displayed to the author, who can select one of the items to add it to the page.