Source Domain Behavior Mapping for Web Content Tailoring
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Solution Overview
Problem
Current technologies lack effective methods to understand and tailor user behavior across different source domains, making it difficult to customize the web page experience based on the source domain from which a user navigates, which is crucial for informing business decisions and improving user interaction within a website.
Innovation Solution
The method involves compiling information about source domains and user behavior patterns, filtering page view records, grouping sessions into clusters, and using entropy calculations to map source domains to behavior patterns, allowing for the presentation of tailored page views based on predicted user behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If web functionality becomes more complex with multiple sharing channels, then content sharing capability is improved, but understanding user browsing and consumption dynamics becomes more difficult
Solution Approach 1:
The patent segments user sessions based on source domain categories (social networks, blogs, news articles, etc.) and analyzes navigation patterns within each segment separately. This allows the system to handle complex multi-channel sharing by breaking down the overall user behavior into manageable source-specific patterns, making it possible to understand and measure dynamics across different channels.
Solution Approach 2:
The patent introduces source domain categorization as an intermediary layer between the complex web of sharing channels and the analysis of user behavior. By categorizing source domains and using these categories as mediators to predict navigation patterns, the system can process the complexity of multiple sharing channels without losing the ability to measure and understand user dynamics.
2Measurement precision
If user behavior data is collected across multiple source domains, then insight accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the data processing task by categorizing source domains into distinct groups (social networks, blogs, news, etc.) and analyzing navigation patterns for each category separately. This segmentation reduces processing complexity by breaking down the overall data analysis into manageable source-specific analyses while maintaining accurate insights through comprehensive coverage of all sources.
Solution Approach 2:
The patent changes the parameter of analysis from individual source domains to source domain categories. By aggregating sources into categories and analyzing patterns at the category level, the system reduces data processing complexity while maintaining insight accuracy through the use of categorical groupings that capture essential behavioral differences.
3Ease of operation
If traditional user modeling approaches are used, then implementation simplicity is maintained, but ability to tailor content based on source domain is reduced
Solution Approach 1:
The patent performs preliminary action by pre-categorizing source domains and pre-analyzing navigation patterns for each source category before actual content delivery. This creates a mapping between source domains and predicted navigation behaviors in advance, allowing the system to simply look up and apply the appropriate pattern when a user arrives from a known source, thus maintaining implementation simplicity while achieving sophisticated source-based tailoring.
Solution Approach 2:
The patent implements feedback by using observed navigation patterns from users of similar source domains to predict and tailor content for current users. The system continuously learns from user behavior data and refines the mapping between source domains and navigation patterns, enabling increasingly accurate source domain-based content tailoring while maintaining a relatively simple implementation through pattern matching.
Data Source
AI summary
A method for tailoring content in a web page is provided. There is a relationship between the source domain a user comes from and the behavior pattern of a user on a website. To predict the behavior patterns of a particular user coming from a particular source domain, first a large dataset is compiled from user logs. Second, session analysis is performed on the dataset to identify sessions, session characteristics, page view categories, and source categories. Third, sessions are clustered and analyzed to determine how the behavior changes according, to a source category. Fourth, a mapping between source categories and behavior patterns is generated. When a user requests a page view from a source domain, if the source domain is mapped to a source category, then a tailored version of the page view is presented to the user based on the mapping between source categories and behavior patterns.