Dynamic Classification Dictionary for User Profiling
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Solution Overview
Problem
Current content advisory systems fail to accurately profile and target users due to their reliance on incomplete data, such as registration information and prior purchase history, which does not account for current interests or behavioral data effectively.
Innovation Solution
A system and method that gather behavioral data from user activities on Web sites, classify documents based on content and contextual meta-data, and combine user profile information with collaborative and editorial data to provide personalized content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional information filtering methods are used (Yellow Pages, broad topic classification), then users can access organized information, but the information is not sufficiently relevant to individual user interests
Solution Approach 1:
The system automatically collects behavioral data from user activities without requiring explicit user input. Users passively generate profile information through their browsing, clicking, and interaction patterns, eliminating the need for manual surveys or registration forms while continuously updating their profiles
Solution Approach 2:
The system continuously monitors user behavioral data and uses this feedback to dynamically update user profiles and refine content recommendations. The profiling system learns from user responses and adjusts future content delivery based on observed patterns
2Loss of information
If explicit user information collection (surveys, questionnaires) is used, then user profile data can be obtained, but user effort and provider resource expenditure increase
Solution Approach 1:
The system automatically collects behavioral data from user activities without requiring explicit user input. Users passively generate profile information through their browsing, clicking, and interaction patterns, eliminating the need for manual surveys or registration forms while continuously updating their profiles
Solution Approach 2:
The system uses cookies and tracking programs as intermediaries to collect user information. These intermediaries automatically capture behavioral data in the background, serving as a mediator between user actions and profile construction without requiring direct user engagement
3Adaptability or versatility
If broad topic classification is used for content organization, then information can be categorized by general subjects, but specific user interests cannot be accurately targeted
Solution Approach 1:
The system segments user interests into specific categories based on behavioral patterns rather than using broad topics. By dividing the user base into distinct segments based on actual activity data, the system can target content to specific interest groups with greater precision
Solution Approach 2:
The system applies different classification granularities to different aspects of user behavior. Rather than using a single broad classification system, it creates detailed, localized profiles that capture specific interests in different domains based on where the user has demonstrated engagement
Data Source
AI summary
A dynamic classification dictionary is built for use in profiling and targeting users for additional relevant content. Behavioral data is gathered from user activity, and user documents and actions are categorized. Author-generated document classification information is analyzed and assigned a first taxonomic noun to characterize the document. User-generated tags characterizing a portion of the document are assigned a second taxonomic noun. Search terms that resulted in the user accessing the document are identified and assigned a third taxonomic noun. Attributes related to the manner in which the document was accessed are evaluated and assigned a fourth taxonomic noun. The document is processed using pattern rules to extract a fifth taxonomic noun. The taxonomic nouns are aggregated into a composite set of taxonomic nouns, and the dynamic classification dictionary is built by storing the composite set of taxonomic nouns.