Document Ranking by User Topic Familiarity and Comprehensibility
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Solution Overview
Problem
Existing search engines sort documents based on popularity, neglecting the user's comprehension level, leading to technically detailed documents being prioritized over more comprehensible ones, which can be frustrating and inefficient for users.
Innovation Solution
Assign comprehensibility scores to documents based on sections of text associated with taxonomy topics and user familiarity scores, sorting documents to prioritize those most comprehensible to the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If documents are sorted by popularity (number of references), then documents with higher reference counts are presented first, but documents that are more comprehensible to the user are not necessarily prioritized
Solution Approach 1:
The patent changes the sorting parameter from popularity metrics (reference counts) to comprehensibility metrics (user familiarity with taxonomy topics). Instead of using PAGERANK-style algorithms that count links, the system calculates comprehensibility scores based on the user's known familiarity with topics covered in each document, fundamentally changing what parameter drives the sorting order
Solution Approach 2:
The system incorporates user feedback about topic familiarity into the sorting mechanism. By tracking what topics the user is familiar with and using this information to score document comprehensibility, the system creates a feedback loop where user knowledge state directly influences document presentation order, making the sorting adaptive to individual user needs
2Loss of information
If technically detailed documents are prioritized, then information depth is improved, but user comprehension becomes more difficult
Solution Approach 1:
The patent applies local quality by differentiating document presentation based on the specific user's knowledge state. Instead of a uniform sorting approach for all users, the system tailors the comprehensibility score calculation to each user's familiar topics, ensuring that documents are evaluated and ranked according to their local relevance to that user's comprehension level
3Measurement precision
If documents are sorted by reference count, then popularity is measured accurately, but comprehensibility information is lost
Solution Approach 1:
The patent introduces comprehensibility scores as an intermediary metric between document content and user presentation. Rather than directly using reference counts or raw document properties, the system creates an intermediate comprehensibility score that incorporates both document taxonomy information and user familiarity, serving as a mediator that translates document characteristics into user-relevant ranking information
Data Source
AI summary
Provided are a computer program product, system, and method for sorting documents according to comprehensibility scores determined for the documents. A document has sections of text associated with topics. A determination is made of familiarity scores for a user for the topics associated with the sections of text in the document. A determination is made of a comprehensibility score of the document, indicating comprehensibility of the document to the user, as a function of the determined familiarity scores for the topics in the document. The comprehensibility score for the document is used to determine an order in which to present the document to the user with a group of documents.