Personalization Engine Using Taxonomic Nouns for User Profiling
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Solution Overview
Problem
Current content advisory systems fail to accurately profile and target users due to lack of updated demographic information and behavioral data, which limits their ability to provide relevant digital content beyond immediate product purchases.
Innovation Solution
A system and method that gathers behavioral data from user activities on Web sites, classifies documents, and combines user profile information with collaborative and editorial data to build accurate user profiles, using taxonomic nouns to identify and deliver targeted content based on user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional content advisory systems use basic demographic information and purchase behavior data, then the system implementation is simple, but the user profiling accuracy and content relevance are insufficient
Solution Approach 1:
The patent segments user profiling into multiple dimensions: demographic information, purchase behavior, browsing behavior, and social interactions. Each dimension is processed separately through dedicated modules (profile generator, behavior analyzer, interest detector) and then integrated to form a comprehensive user profile, improving accuracy while maintaining manageable system complexity through modular design
Solution Approach 2:
The patent combines multiple data sources (demographic data, purchase history, browsing patterns, social network information) and multiple analysis methods (collaborative filtering, content-based filtering, behavioral analysis) into a unified profiling system. This integration of diverse information streams creates a more accurate and comprehensive user profile than any single method could achieve alone
2Measurement precision
If the system collects and analyzes extensive behavioral data from multiple sources, then the user profile accuracy improves, but the data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-processing and storing user behavior data, browsing patterns, and purchase history in structured formats before actual recommendation generation. User profiles are continuously updated in the background with aggregated behavioral data, so when content recommendations are needed, the system can quickly query pre-computed profiles rather than analyzing raw data in real-time
Solution Approach 2:
The patent employs feedback mechanisms where user interactions with recommended content (clicks, purchases, ratings, time spent) are continuously monitored and fed back into the profiling system. This feedback loop refines user profiles over time, improving recommendation accuracy while the system learns to prioritize the most predictive behavioral signals, reducing unnecessary processing
3Ease of operation
If the system uses broad subject area categorization for content, then the system implementation is straightforward, but the ability to locate specific information of interest to users is insufficient
Solution Approach 1:
The patent applies local quality by moving from uniform broad categorization to granular, context-specific tagging. Individual content items are tagged with multiple precise keywords and attributes relevant to their specific content (e.g., specific product features, article topics, video themes) rather than relying solely on hierarchical category classifications. This allows users to locate specific information through multiple precise entry points while the system maintains the broader category structure for organizational purposes
Data Source
AI summary
User profiles are created based on taxonomic nouns related to documents accessed by the user. The profiles can be leveraged to create lists, such as mailing lists and lead lists, to target content, such as offers, to persons most likely to be interested in the content. A database of the profiles is queried based on nouns describing content to be promoted. The profiles that satisfy the query are used to generate a list. The invention can be used to create any type of list, such as mailing lists, lead lists, lists of related content, lists of related users, lists of categorized content, and the like.