Contextual Segmentation of Information Objects via Dynamic Topic Modeling
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Solution Overview
Problem
Existing methods of contextual segmentation for web pages and user profiles lack scalability and fail to quickly adapt to current events and user interests, leading to inefficient targeted advertising.
Innovation Solution
A system and method for contextually segmenting information objects using dynamically derived topics and contextual matching with an indexing engine, such as Sphinx or Solr, to create and assign categories based on n-gram analysis and user profiles, allowing for flexible and scalable categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing fixed category methods are used for contextual segmentation, then implementation simplicity is maintained, but adaptability to current events and user interests deteriorates
Solution Approach 1:
The patent implements dynamic topic modeling that automatically adapts categories to current events and user interests. The system uses probabilistic topic models that are continuously trained on incoming data, allowing categories to evolve over time rather than remaining fixed. This enables the segmentation system to respond to changing user behavior patterns and emerging topics while maintaining a structured approach through the probabilistic framework.
Solution Approach 2:
The system performs self-service through automated topic discovery and category generation. The probabilistic topic model automatically identifies new topics and creates relevant categories without manual intervention. The system self-updates its category structure by analyzing user interactions and content patterns, eliminating the need for manual category maintenance while adapting to new information automatically.
2Measurement precision
If comprehensive contextual analysis is performed on all information objects, then segmentation precision is improved, but processing time increases
Solution Approach 1:
The patent applies partial action by performing contextual analysis selectively rather than comprehensively on all information objects. The probabilistic topic model identifies and focuses analysis on the most relevant features and terms that contribute to accurate segmentation, ignoring less important elements. This allows the system to achieve high segmentation precision by concentrating computational resources on the most informative aspects of each information object.
Solution Approach 2:
The system changes parameters by using probabilistic thresholds and confidence scores to determine the depth of analysis required for each information object. Based on the topic distribution and relevance scores, the system dynamically adjusts the level of contextual analysis performed, applying more rigorous analysis only when necessary to achieve accurate segmentation while maintaining efficiency for routine cases.
3Adaptability or versatility
If dynamic topic modeling is implemented for contextual segmentation, then adaptability to changing interests is improved, but computational complexity increases
Solution Approach 1:
The patent replaces manual category management mechanisms with automated probabilistic topic modeling. Instead of mechanically updating categories through manual processes, the system uses statistical models that automatically discover topics and update category structures based on data patterns. This substitution of mechanical operations with probabilistic computation enables dynamic adaptability while managing complexity through automation.
Solution Approach 2:
The probabilistic topic model serves as an intermediary between raw data and category assignments. Rather than directly mapping information objects to categories, the topic model acts as a mediating layer that discovers underlying themes and structures. This intermediary processing simplifies the overall system architecture by providing a structured probabilistic framework that handles the complexity of dynamic category generation automatically.
4Productivity
If fixed category sets are used for segmentation, then system simplicity is maintained, but responsiveness to current events deteriorates
Solution Approach 1:
The patent implements dynamic category structures that automatically update to reflect current events and trending topics. The probabilistic topic model continuously analyzes incoming data to identify emerging themes, allowing the category system to adapt in real-time to changing user interests and current events. This dynamic approach enables the system to maintain high advertising performance by ensuring categories remain relevant to current user behavior patterns.
Data Source
AI summary
Systems and methods are disclosed for contextual analysis and segmentation of information objects. In accordance with one implementation, information objects, such as web pages and user profiles, may be processed to obtain a list of key terms. An index may be created containing each of the information objects and associated key terms. Information objects may then be matched to contextual segments (i.e., categories of information objects) by submitting terms associated with the contextual segments against the index. Further, thresholding may be applied, so that only the most relevant information objects for a contextual segment are assigned to the contextual segment.