User Profile Update via Content Significance Detection
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Solution Overview
Problem
Existing systems fail to effectively identify and personalize electronic content significant to a user, leading to a lack of tailored recommendations and inefficient user experience.
Innovation Solution
Implementing an "I Like This" button that allows users to indicate significant content, which analyzes the content and updates a user profile based on user interactions, using techniques like term frequency-inverse document frequency, metadata inspection, and visual pattern recognition to determine and prioritize relevant topics and content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a user profile is generated automatically based on online activities, then the system can provide customized user experience, but the system cannot accurately identify specific content significant to the user
Solution Approach 1:
The patent implements feedback mechanisms where users explicitly indicate which content is significant to them through interactions such as clicking, bookmarking, or rating content. This feedback is then used to refine the user profile and improve the accuracy of content significance identification, resolving the contradiction between automated profile generation and precise content identification.
Solution Approach 2:
The system performs preliminary analysis of user interactions and content characteristics before generating the final user profile. By pre-processing user behavior data and content metadata, the system prepares refined information that enables more accurate identification of significant content, bridging the gap between automated profiling and precise content significance detection.
2Loss of information
If the system analyzes all content to identify topics, then comprehensive user interests can be captured, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the most relevant features and topics from content rather than analyzing entire documents. By identifying and extracting key topical elements, the system captures comprehensive user interests while significantly reducing processing time and computational resources required for full content analysis.
Solution Approach 2:
The system performs partial analysis of content by focusing on specific sections, keywords, or metadata that are most indicative of user interest. This selective approach allows the system to capture essential user preferences without the overhead of complete content processing, balancing information coverage with processing efficiency.
3Measurement precision
If the user profile is updated frequently based on user interactions, then the personalization accuracy improves, but the system complexity and processing overhead increase
Solution Approach 1:
The patent implements periodic updates to the user profile based on accumulated user interactions rather than continuous real-time updates. By updating profiles at regular intervals or after reaching certain interaction thresholds, the system maintains high personalization accuracy while reducing system complexity and processing overhead associated with frequent updates.
Solution Approach 2:
The system merges multiple user interaction signals and content analysis results into a single consolidated profile update operation. By combining multiple data sources and processing steps into unified update routines, the system achieves high personalization accuracy while minimizing the complexity increment that would result from multiple separate update mechanisms.
Data Source
AI summary
Content that is significant to a user may be determined. An indication that a user finds content within a document significant may be received. In response to the received indication, the document may be analyzed to identify a set of topics associated with the content of the document. From the set of topics, a subset of topics responsible for the user finding the document significant may be identified. A user profile associated with the user may be updated based on the subset of topics.