Search Ranger Multi-Layer Spam Detection Model
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Solution Overview
Problem
Search spam techniques, such as redirection spam, exploit weaknesses in existing search engine ranking algorithms, leading to undeserved high rankings and decreased search efficiency, burdening legitimate advertisers and web users.
Innovation Solution
The Search Ranger system employs a multi-layer model with self-monitoring and self-protection components to identify and defend against spam by correlating search results, tracking redirection patterns, and strengthening ranking algorithms, using techniques like similarity-based grouping and anti-cloaking to detect and mitigate spam.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If search engines use traditional ranking algorithms, then search results can be generated quickly, but search spammers can exploit weaknesses to obtain undeserved high rankings
Solution Approach 1:
The patent segments the ranking process into multiple independent components: traditional ranking algorithms for speed and the Search Ranger system for spam detection. The spam verification component analyzes search results separately and provides corrections, allowing the main search engine to maintain high productivity while improving ranking reliability through modular spam filtering.
Solution Approach 2:
The patent introduces an intermediary spam verification component that acts as a mediator between the traditional ranking algorithm and the final search results. This intermediary analyzes rankings, detects spam patterns, and adjusts results without replacing the core ranking system, thereby maintaining speed while improving accuracy.
2Reliability
If search engines implement comprehensive spam detection, then search quality improves, but system complexity increases
Solution Approach 1:
The spam detection system is segmented into distinct functional modules: spam verification component, multi-layer model, bottleneck layer identifier, and correction mechanism. Each module performs a specific function, making the overall complex system manageable and maintainable while delivering comprehensive spam detection.
Solution Approach 2:
The patent creates a universal spam detection framework that can identify multiple types of spam (redirection spam, doorway pages, link farms) using a single multi-layer model. This multi-functional approach improves search quality without proportionally increasing complexity, as one system handles diverse spam patterns.
3Measurement precision
If search engines monitor and analyze all search results for spam, then spam detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by focusing spam detection efforts on specific high-risk areas: identifying bottleneck layers in the spam model and targeting corrections at those critical points. Rather than uniformly analyzing all search results, the system concentrates resources on where spam patterns are most concentrated, improving detection accuracy while reducing overall processing time.
Solution Approach 2:
The spam verification component operates autonomously, automatically analyzing search results, identifying spam patterns, and applying corrections without requiring manual intervention. This self-service capability maintains high detection accuracy while minimizing the time overhead by eliminating human review steps.
4Productivity
If search engines take corrective actions against identified spam, then search efficiency improves, but potential false positives may occur
Solution Approach 1:
The patent implements feedback mechanisms where the spam verification component continuously monitors the effectiveness of corrective actions. By analyzing whether corrections improve or degrade search quality, the system can adjust its detection thresholds and reduce false positives while maintaining search efficiency improvements.
Solution Approach 2:
The system takes preliminary anti-action by pre-identifying spam patterns and bottlenecks before they significantly impact search results. By detecting and correcting spam proactively rather than reactively, the system improves search efficiency while reducing false positives through early intervention with verified spam patterns.
Data Source
AI summary
An exemplary method for protecting web browsers from spam includes providing a multi-layer model that includes a doorway layer, a redirection domain layer, an aggregator layer, a syndicator layer and an advertiser layer; identifying domains as being associated with at least one of the layers; and, based at least in part on the identifying, taking one or more corrective actions to protect web browsers from search spam. An exemplary method for identifying a bottleneck layer in a multi-layer spam model includes providing a multi-layer spam model, collecting spam advertisements, associating a block of IP addresses with the collected spam advertisements and identifying a bottleneck layer based on the block of IP addresses. Other methods, systems, etc., are also disclosed.