Content Ranking System for Web Portal Personalization
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Solution Overview
Problem
Web portals face challenges in personalizing content for users based on their historic behavior due to scalability issues and limitations in existing customization approaches that require active user participation and are inflexible regarding content types.
Innovation Solution
A content ranking and prediction system with three main components: a web portal and toolbar component for data collection, a modeling component that generates content scoring functions based on user event data and content features, and a scoring component that ranks content items in real-time, allowing for personalized content delivery across multiple sections of a portal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If web portals customize content for users by grouping content per language or allowing customization of news sections, then user personalization is improved, but storage requirements and processing power increase causing scalability issues
Solution Approach 1:
The patent segments users into clusters based on their behavior patterns and content preferences. Instead of maintaining separate content configurations for each individual user, users are grouped into segments (clusters) that share similar characteristics. This segmentation approach reduces the overall storage and processing requirements while still providing personalized content delivery, as the system only needs to maintain content configurations for each segment rather than for every individual user.
Solution Approach 2:
The patent changes the parameter of user identification from individual user IDs to cluster IDs. By representing users as members of behavior-based clusters rather than as individual entities, the system reduces the dimensionality of personalization data. This parameter change allows the portal to scale more efficiently while maintaining personalization capabilities, as the number of clusters is significantly smaller than the number of individual users.
2Device complexity
If web portals treat every visitor in the same manner and show the same content, then system complexity is reduced, but user personalization and engagement are limited
Solution Approach 1:
The patent performs preliminary action by pre-computing user behavior patterns and organizing users into clusters before content delivery. The system analyzes user interactions with content and pre-segments users based on their historic behavior, content preferences, and engagement patterns. This preliminary clustering allows the portal to quickly retrieve and deliver personalized content without performing complex real-time analysis for each user request, thus maintaining low system complexity during content delivery while still providing personalized experiences.
3Adaptability or versatility
If web portals require active user participation for content customization, then content relevance to user interests is improved, but ease of operation decreases and user burden increases
Solution Approach 1:
The patent implements self-service by enabling the system to automatically perform content personalization without requiring active user participation. The portal autonomously analyzes user behavior patterns, segments users into clusters, and delivers personalized content based on cluster characteristics. Users passively benefit from personalized content delivery based on their historic interactions with the portal, eliminating the need for them to manually configure preferences or actively participate in the customization process.
4Device complexity
If web portals limit customization to specific content types or sections, then implementation complexity is reduced, but adaptability across different content types is limited
Solution Approach 1:
The patent applies universality by creating a unified clustering framework that works across all content types and portal sections. The behavior-based clustering system is content-agnostic and can analyze user interactions with any type of content (news articles, videos, products, etc.). The same clustering algorithm and personalization mechanism are universally applied across the entire portal, allowing the system to handle diverse content types without requiring separate customization implementations for each content category.
Data Source
AI summary
A method and apparatus for customizing content presented to individual users or user segments is provided. There may be three components, a web portal and toolbar component, a modeling component, and a scoring component. The web portal and toolbar component presents content items and collects data. The web portal and toolbar component generates user event data based on the user actions. The user event data is forwarded to the modeling component. The modeling component generates content scoring functions based on user event data and attributes of content items. Content scoring functions may be unique to individual user segments. The content scoring functions based on content features generate probability a content item will be viewed. The scoring component decides which content items are placed in a portal. The scoring component uses the scoring functions generated by the modeling component to rank content items in real time.