Content Recommendation Engine for Document Authoring
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Solution Overview
Problem
Users of document authoring applications like Microsoft Word face inefficiencies in finding relevant content, as the list of recent files may not be relevant, requiring manual searching through folders.
Innovation Solution
A system and method that utilizes a content recommendation engine to identify and present relevant documents based on user feedback, using machine learning models to analyze metadata, keywords, and user behavior, and shares these recommendations on a collaborative content sharing platform, allowing users to provide feedback on suggested documents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a list of recent files is provided in the document authoring application, then users can access previously generated documents, but the list may not be relevant to the current document being edited, requiring manual searching
Solution Approach 1:
The system implements feedback loops where user interactions with suggested documents (selections, views, edits) are continuously monitored and fed back into the machine learning model. This feedback mechanism allows the recommendation engine to learn from user behavior patterns and improve the relevance of suggested documents over time, resolving the contradiction between automated suggestions and relevance accuracy
Solution Approach 2:
The system dynamically changes parameters such as weighting factors for different document attributes (metadata, keywords, user behavior patterns) based on the current editing context. By adjusting these parameters in real-time according to the document being edited and user preferences, the system provides contextually relevant suggestions rather than static recent file lists, improving both productivity and relevance
2Ease of operation
If users manually search through folders to find relevant content, then they can locate specific documents, but this process is time-consuming and reduces productivity
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing document metadata, keywords, and content features before users need to search. The machine learning model pre-generates recommendation lists based on current editing context, so when users need content suggestions, the system can immediately present relevant documents without requiring users to manually browse through folders, thus saving time while maintaining ease of operation
Solution Approach 2:
The recommendation system operates autonomously by automatically analyzing the current document context, user behavior patterns, and document repository to generate and update suggestions without user intervention. The system serves itself by continuously learning from user interactions and automatically improving its recommendation accuracy, eliminating the need for manual searching while preserving full search capability when needed
Data Source
AI summary
A system and method for summarizing suggested content and sharing the summarized suggested content is described. In one aspect, a computer-implemented method includes performing an analysis of text of a document, searching a document library for content elements and documents based on the analysis of the text, identifying candidate documents and candidate content based on the searching, presenting a list of candidate documents or candidate content with the document authoring application, receiving a selection of a candidate document or candidate content from the list in the document authoring application, and providing the selected candidate document to a collaborative content sharing platform, the collaborative content sharing platform configured to generate a graphical user interface that displays a list of shared documents, the shared documents includes candidate documents selected by one or more users of a group of users that share access to the collaborative content sharing platform.