Personalized Content Selection Algorithm with User-Modifiable Rules
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Solution Overview
Problem
Current content personalization technologies fail to effectively combine adaptive computation with user control and encompass a wide range of rules for individual preferences, such as content source, topic, style, and popularity, in a flexible and personalized algorithm.
Innovation Solution
A method and system for selecting personalized content using an evaluation tool that generates and modifies content selection algorithms based on user interaction, allowing users to view and modify rules, and incorporates weights and optimization techniques to combine various selection rules into a personalized algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If adaptive computation techniques (neural nets, genetic algorithms) are used for content personalization, then the system can automatically learn user preferences, but users cannot see or modify the rules being used to select content
Solution Approach 1:
The patent segments the content selection system into distinct rule components that can be individually viewed and modified by users. Instead of presenting a monolithic adaptive algorithm, the system breaks down the selection logic into separate rules (e.g., topic preferences, source preferences, time-based rules) that users can inspect and adjust independently, thereby maintaining automation while enabling user control.
Solution Approach 2:
The patent introduces an intermediary layer between the adaptive computation engine and the user interface. This intermediary translates complex algorithmic decisions into comprehensible rules that users can understand and modify, bridging the gap between automated processing and user-friendly control without sacrificing the power of adaptive techniques.
2Ease of operation
If editors hand-select content for each individual user, then content can be highly personalized, but this is not scalable to large numbers of users
Solution Approach 1:
The patent implements a self-service system where users can independently view, understand, and modify their own content selection rules without requiring manual intervention from editors. The adaptive computation engine automatically processes user preferences and generates personalized content selections, enabling high-quality personalization to scale to large user populations without proportionally increasing editorial workload.
3Ease of operation
If users manually customize their own content preferences, then they have full control, but most consumers are not willing to invest sufficient effort up front
Solution Approach 1:
The patent performs preliminary action by automatically generating initial content selection rules and algorithms based on user profiles, browsing history, and interaction data before users need to make any decisions. The system pre-configures reasonable defaults and allows users to review and modify these pre-generated rules at their convenience, eliminating the need for users to invest significant upfront effort while maintaining their ability to control preferences.
4Measurement precision
If a wide range of varied rules are created to accommodate each individual user's preferences, then personalization accuracy improves, but the complexity of the selection algorithm increases
Solution Approach 1:
The patent segments the complex selection algorithm into multiple independent, modular rules that each handle specific aspects of user preferences (topic preferences, source preferences, temporal preferences, etc.). This segmentation allows the system to accommodate a wide range of varied rules for high personalization accuracy while managing complexity through modular design, where each rule can be developed, tested, and modified independently.
Data Source
AI summary
The invention relates to a method and system for selecting personalized content for a user, the method being performed by an evaluation tool instantiated on a computing device and comprising the evaluation tool. The evaluation tool creates a content selection rule for the user for finding and filtering content items, such as advertising content. The tool generates a content selection algorithm from the content selection rule for determining which content items to present to the user and presents the content item to the user based on the content selection algorithm and allows the user to interact with the presented content item.