Behavioral Classification Engine for Ad Targeting
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Solution Overview
Problem
Existing methods for characterizing Internet user behavior are limited by relying on self-reported information and click behavior, failing to provide comprehensive insights into user behavior on the Internet.
Innovation Solution
A system that classifies pages viewed by users into topics, tracks page counts and recency, and characterizes users into behavioral segments based on these metrics to serve targeted advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If self-reported information and click behavior are used to characterize users, then the characterization method is simple to implement, but the comprehensiveness and accuracy of user behavior insights are limited
Solution Approach 1:
The patent introduces a classification engine as an intermediary component that automatically categorizes web pages into topics and hierarchies. This mediator processes the raw browsing data and transforms it into structured behavioral segments, thereby improving measurement precision without requiring direct complex analysis of all raw data by the advertising system.
Solution Approach 2:
The patent segments user browsing behavior into distinct behavioral segments based on topic classifications and browsing patterns. By dividing the continuous browsing data into discrete behavioral categories (such as informational, transactional, recreational segments), the system achieves more precise user characterization while managing complexity through structured segmentation.
2Measurement precision
If comprehensive browsing data is collected and analyzed, then user behavior characterization accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The classification engine performs preliminary categorization of web pages into topics and hierarchies before the advertising decision is needed. By pre-processing and classifying browsing data as users navigate the web, the system prepares behavioral segments in advance, reducing the time required for real-time advertising decisions while maintaining comprehensive analysis accuracy.
3Adaptability or versatility
If detailed page classification and tracking is implemented, then advertising targeting precision is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The classification engine serves multiple functions simultaneously: it classifies pages into topics, determines page hierarchies, tracks browsing patterns, and generates behavioral segments. This multi-functional approach enables sophisticated advertising targeting capabilities while avoiding the need for separate specialized systems for each function, thereby managing overall system complexity.
Data Source
AI summary
A plurality of pages viewed by a communications network user (e.g., an Internet user) are classified as pertaining to one of a plurality of topics. A count of each of the pages viewed by the communications network user for each of the topics is tracked, as is a recency with which each of the pages viewed by the communications network user was viewed for each of the topics. The communications network user is characterized as belonging to one or more behavioral segments based on the count and the recency. Advertisements are served to the communications network user based on at least advertising targeting parameters and the characterization.