User Profile Construction via Document Consumption Analysis
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Solution Overview
Problem
Content recommendation systems often require extensive user input and are imprecise, as they ask users to select interests they think they have rather than actual interests, making the process time-consuming and inefficient.
Innovation Solution
A method to develop a user profile by analyzing documents consumed by a user, creating document summaries with concept features and relative strengths, and blending these with current document summaries to recommend relevant content, using a system with a memory and data processor to store and process user profiles and document summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If collaborative filtering is used to improve recommendation power, then recommendation accuracy is improved, but user information requirements increase
Solution Approach 1:
The system performs preliminary analysis of user consumption behavior (documents read, time spent, scrolling patterns) to automatically build user profiles before recommendations are needed. This preliminary action captures actual user interests through behavioral data rather than requiring users to manually input information, thereby reducing information requirements while maintaining recommendation accuracy.
2Loss of information
If users are asked to submit their interests manually, then user input is obtained, but time consumption increases and precision decreases
Solution Approach 1:
The system enables self-service by automatically inferring user interests from consumption behavior patterns without requiring manual user input. The system processes user reading habits, time spent on documents, and scrolling behavior to autonomously build and update user profiles, eliminating time-consuming manual input while capturing accurate user interest information.
Solution Approach 2:
The patent replaces the mechanical process of manual user input with an automated computational system that analyzes consumption behavior. Instead of users manually submitting interest information, the system uses algorithms to process behavioral data (reading patterns, time spent, document interactions) and automatically generate user profiles, substituting manual effort with automated information processing.
3Ease of operation
If content-based filtering is used to reduce user input, then ease of operation is improved, but recommendation scope is limited
Solution Approach 1:
The system adds a new dimension to content-based filtering by incorporating temporal and behavioral characteristics into the analysis. Instead of only analyzing document content features, the system dimensions the user profile to include time spent on documents, scrolling behavior, and reading patterns, thereby expanding the recommendation scope while maintaining ease of operation with minimal user input.
Data Source
AI summary
A method develops a user profile to indicate a user's topics of interest. Documents considered by a computer system to have been consumed by first user are identified. Document summaries are developed identifying concepts represented in each document and relative strength by which the concept is considered associated with the document, and are accumulated from all the identified documents into a profile for the first user. The user profile is stored in a user profile database in a storage medium accessible to the computer system. A system develops a user profile to indicate the user's topics of interest by identifying documents consumed by a user with a document summary for each document identifying concepts and their relative strength. Document summaries are accumulated into a profile for first user.