Contextual Segmentation Using Dynamically-Derived Topics
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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, where a processor generates contextual representations of web pages and user profiles, identifies similarities, and creates segments based on these representations, allowing for dynamic categorization and optimized advertising.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing methods of contextual segmentation are used, then segmentation can be performed, but scalability is insufficient for the ever-increasing number of web pages and users
Solution Approach 1:
The system segments information objects into contextual segments based on dynamically derived topics. Each contextual segment represents a subset of information objects sharing similar contextual representations, enabling scalable processing by dividing the large space of all possible segments into manageable topic-based groups
Solution Approach 2:
The system uses dynamically derived topics instead of static categories. Topics are generated on-demand based on the actual content of information objects, allowing the segmentation system to adapt to new web pages and users without requiring pre-defined categories, thereby improving scalability
2Adaptability or versatility
If a fixed set of categories is used, then categorization is simple, but the system cannot quickly respond to current events and interests
Solution Approach 1:
The system generates topics dynamically based on the content of information objects rather than using a fixed category list. When new information objects are processed, the system extracts topics from their contextual representations in real-time, enabling immediate adaptation to current events and user interests without waiting for manual category updates
Solution Approach 2:
The system automatically derives topics from the information objects themselves without requiring external intervention or manual category definition. The contextual analysis process self-generates appropriate topics based on the content, allowing the system to autonomously adapt to new content and events
Data Source
AI summary
Systems and methods are disclosed for contextual analysis and segmentation of information objects. According to one implementation, information objects, such as web pages and user profiles, may be analyzed to identify key terms. These key terms may be included in a contextual representation of an information object. By comparing the contextual representations of a plurality of information objects, one or more contextual segments (i.e., categories of information objects) may be created. Each contextual segment may also be associated with its own contextual representation. Once a contextual segment has been created, information objects may be assigned to the contextual segment. These contextual segments may be used to deliver targeted advertising, for example.