Content Suggestion System Using Search Log Feedback
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Solution Overview
Problem
Existing advertising technologies lack effective methods to suggest relevant content sources for online advertisements based on user behavior and keyword inputs, failing to accurately target audiences.
Innovation Solution
A content suggestion system that processes user behavior data from search logs to rank and suggest web-sites for advertisers, using keyword engines to identify content vectors and deliver suggested sites based on popularity and user interaction metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content sources are suggested based on pre-defined categories and URL similarity, then advertisers can identify relevant web-sites, but the system lacks effectiveness in accurately targeting audiences based on actual user behavior
Solution Approach 1:
The system implements feedback by analyzing user behavior data from search logs to determine which web-sites users actually select when presented with content sources. This behavioral feedback loop allows the system to learn from actual user interactions and improve targeting accuracy over time, moving beyond static pre-defined categories to dynamic, behavior-based recommendations
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns by processing search logs in advance to identify correlations between queries and web-site selections. This preliminary action creates a foundation of behavioral insights that enables more accurate real-time content source suggestions without requiring complex real-time computation during ad delivery
2Productivity
If the system processes user behavior data from search logs to rank web-sites, then ad placement effectiveness improves, but system complexity increases
Solution Approach 1:
The system extracts only the necessary behavioral signals from search logs - specifically query-web-site selection pairs - rather than processing entire user sessions or all available data. This extraction approach focuses computational resources on the most relevant indicators of user preference, improving ad placement effectiveness while controlling system complexity
Solution Approach 2:
The patent introduces an intermediary ranking mechanism that translates raw search log data into simplified web-site rankings based on selection frequency. This intermediary layer acts as a mediator between complex user behavior data and ad targeting decisions, making the system more manageable while still capturing essential behavioral patterns
Data Source
AI summary
A system and related methods suggest content based on user input and another metric. In one implementation, web-sites are suggested to advertisers in response to keyword input and by factoring in how often such web-sites were selected or “clicked on” as a result of corresponding search queries. Search logs are processed to determine how often certain query terms led to web-sites being selected. Web-sites are ranked accordingly. Keywords from advertisers are matched to the web-site rankings to present the top web-sites.