Personalized Content Delivery System with User Preference Thresholds
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Solution Overview
Problem
Existing techniques for information distribution in computer networks focus on popularity rather than personal relevance, making it difficult for users to efficiently find relevant information from large data sources like RSS feeds, news, or social media streams.
Innovation Solution
A system that collects explicit and implicit user feedback to classify topics and deliver content only when they exceed a user's importance threshold, using a model to predict relevance and adapt to user preferences over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If statistical methodologies are used to determine popular information, then information relevance to large numbers of users is improved, but personal relevance of information to individual users deteriorates
Solution Approach 1:
The patent segments the monolithic approach of statistical popularity ranking into personalized segments for each user. It creates individual user profiles that divide the information filtering task into user-specific subsets, allowing each user to receive customized information based on their unique preferences rather than a generic popularity-based ranking.
Solution Approach 2:
The system dynamically adapts to each user's evolving preferences by continuously learning from explicit feedback (user actions like clicking, reading, or dismissing information) and implicit feedback patterns. This dynamic adaptation allows the information delivery system to transform from a static popularity-based model to a flexible, user-specific model that evolves over time.
2Quantity of substance
If all content from multiple data sources is delivered to users, then information completeness is improved, but information overload and user efficiency deteriorates
Solution Approach 1:
The patent extracts only the most relevant information from multiple data sources based on user preferences and characteristics. Instead of delivering all available content, the system selectively extracts and delivers information that matches user profiles, thereby maintaining information completeness for relevant topics while eliminating irrelevant content that would cause overload.
Solution Approach 2:
The system implements feedback loops where user interactions with delivered information (explicit feedback through actions and implicit feedback through usage patterns) continuously refine the relevance assessment. This feedback mechanism allows the system to learn from user responses and progressively improve information selection, ensuring that delivered content remains both complete and efficiently targeted.
3Ease of operation
If traditional search and manual filtering are used, then user control over information selection is improved, but time consumption and operational complexity deteriorates
Solution Approach 1:
The patent performs preliminary actions by proactively analyzing user preferences, monitoring data sources, and pre-filtering information before users need to search or manually filter. The system anticipates user information needs by continuously learning preferences and pre-processing content from multiple sources, so that relevant information is ready for immediate delivery without requiring user time investment in search or filtering operations.
Solution Approach 2:
The system enables self-service information filtering where the automated system performs the filtering and selection tasks that would otherwise require user intervention. By implementing automated preference learning and content filtering mechanisms, the system serves itself in identifying and delivering relevant information, freeing users from time-consuming manual search and filtering activities while maintaining their control over information preferences.
Data Source
AI summary
A non-transitory computer readable storage medium includes instructions to collect explicit feedback from a user regarding user content preferences. Multiple data sources are monitored. Topics associated with the multiple data sources are classified. The importance of the topics to the user is characterized. Content is delivered to the user when a selected topic exceeds an importance threshold for the user. Implicit feedback from the user that characterizes refined user content preferences is tracked. The instructions to characterize the importance of topics evaluates the explicit feedback and the implicit feedback.