Behavioral Widget for Affiliate Content Selection
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Solution Overview
Problem
Personalization systems for large websites are expensive to implement and maintain, and often rely on limited behavioral data, which restricts the quality and relevance of personalized content recommendations.
Innovation Solution
A system utilizing web page widgets to collect and analyze user activity data across multiple websites, generating behavioral associations and content recommendations without requiring extensive infrastructure, by aggregating clickstream data to detect item-to-item relationships and providing personalized content based on user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a sophisticated personalization system is implemented to provide personalized content recommendations, then the quality and relevance of recommendations is improved, but the cost of implementation and maintenance increases significantly
Solution Approach 1:
The patent introduces an intermediary service that collects behavioral data from multiple websites and provides processed recommendations to individual sites. This mediator handles the complex data aggregation and analysis tasks centrally, allowing individual websites to offer personalized recommendations without building expensive infrastructure themselves. The service acts as a bridge between data sources and recommendation consumers.
Solution Approach 2:
The patent combines behavioral data from multiple independent websites into a unified dataset that is analyzed centrally. By merging data sources and processing capabilities into a single service, the system achieves sophisticated recommendation quality that would be prohibitively expensive for individual sites to replicate independently.
2Quantity of substance
If behavioral data is collected across multiple websites to improve personalization, then the quantity and quality of data increases, but the infrastructure requirements and costs increase
Solution Approach 1:
The patent creates a universal service that performs multiple functions: collecting data from various websites, analyzing behavioral patterns, generating recommendations, and distributing them back to source sites. This multi-functional approach allows a single infrastructure to handle diverse data sources and recommendation needs, reducing overall complexity compared to each site maintaining separate systems.
Solution Approach 2:
The service acts as an intermediary layer between multiple websites and their users, centralizing the complex task of cross-site behavioral data collection and analysis. Individual websites can participate in the program without directly implementing complex tracking infrastructure, as the intermediary handles data aggregation and processing centrally.
3Reliability
If a personalization system is implemented by large companies with extensive resources, then the system can maintain sophisticated infrastructure, but smaller companies cannot afford to implement similar systems
Solution Approach 1:
The patent enables smaller companies to access sophisticated personalization capabilities through a service model where they can self-enroll in the behavioral data collection program by simply adding a widget to their website. The system automatically handles data collection, analysis, and recommendation generation without requiring the smaller company to maintain expensive infrastructure, making reliable personalization accessible to companies of all sizes.
4Device complexity
If limited behavioral data is used for personalization, then implementation costs are reduced, but the quality and relevance of personalized content decreases
Solution Approach 1:
The patent combines behavioral data from multiple websites into a unified analysis framework, allowing the system to achieve high recommendation accuracy without any single website needing to maintain complex infrastructure. By merging data sources centrally, the system overcomes the limitation of limited individual site data while keeping individual participant systems simple.
Data Source
AI summary
A content provider system interacts with a network of web sites to provide behavior-based content to users. Operators of the web sites add widgets to selected web pages of their sites. The widgets, when executed on the computing devices of users who view the selected web pages, report user-generated events to the content provider system. The content provider system analyzes the reported events to detect behavioral associations between particular web sites, web pages, products, and/or other types of items. The widgets may also retrieve and display behavior-based content that is based on these item-to-item behavioral associations. For example, when a user views a particular web page, a widget on that page may request and display descriptions of, and links to, other sites or pages that are (a) behaviorally related to the page being viewed or an item represented thereon, and/or (b) behaviorally related to the past browsing activities of the particular user.