Fraudulent Rating Detection in Application Stores
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Solution Overview
Problem
Application stores face challenges in distinguishing fraudulent user ratings and comments from legitimate ones, leading to inaccurate application rankings and potential harm to users from malicious content.
Innovation Solution
A system and method for automatically detecting fraudulent submissions by analyzing various signals, such as content, user behavior patterns, and submission patterns, to generate intermediate signals that indicate fraudulent activity, which are then combined to conclude on the legitimacy of a submission, allowing for penalties to be imposed on fraudulent accounts or applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user ratings and comments are accepted without verification, then the application store allows free user expression and maintains high submission volume, but fraudulent submissions contaminate the ranking system and mislead users
Solution Approach 1:
The patent introduces an intermediary detection system that sits between submission collection and ranking display. This system analyzes submissions for fraud indicators (bot behavior, suspicious patterns, coordinated attacks) and filters or downweights fraudulent content before it affects rankings, thus maintaining both high submission volume and ranking accuracy
Solution Approach 2:
The system implements feedback loops where submission patterns are continuously monitored, analyzed for fraud indicators, and used to adjust ranking weights. Legitimate submissions receive appropriate weight while fraudulent ones are penalized or excluded, creating a self-correcting system that maintains reliability without reducing productivity
2Measurement precision
If sophisticated fraud detection analysis is implemented, then fraudulent submissions are accurately identified and removed, but system complexity and computational resources increase
Solution Approach 1:
The detection system is segmented into multiple independent analysis modules, each evaluating specific fraud indicators (submission timing patterns, user behavior anomalies, text similarity, coordination patterns). This modular approach improves detection accuracy through comprehensive analysis while managing complexity through clear separation of concerns and independent module development
Solution Approach 2:
The system applies partial analysis to all submissions (basic fraud indicators) and excessive/detailed analysis only to suspicious cases. This tiered approach maintains high detection accuracy for fraudulent content while reducing overall computational complexity by avoiding full-depth analysis of every submission
3Reliability
If fraudulent submissions are detected and penalized, then user trust and ranking integrity improve, but legitimate submissions may be incorrectly penalized
Solution Approach 1:
The system applies preliminary protective measures to legitimate submissions by establishing baseline behavior profiles and confidence thresholds before penalties are applied. Submissions are only penalized when fraud indicators exceed defined thresholds with sufficient confidence, preventing premature or incorrect penalties while maintaining ranking integrity
Solution Approach 2:
The system implements feedback mechanisms where penalty decisions are reviewed and adjusted based on outcomes. When legitimate submissions are incorrectly flagged, the system learns from these false positives and adjusts detection thresholds or weights, continuously improving accuracy and reducing false penalties while maintaining ranking integrity
Data Source
AI summary
The present disclosure describes one or more systems, methods, routines and/or techniques for automatic detection of fraudulent ratings and/or comments related to an application store. The present disclosure describes various ways to differentiate fraudulent submissions (e.g., ratings, comments, reviews, etc.) from legitimate submissions, e.g., submissions by real users of an application. These various ways may be used to generate intermediate signals that may indicate that a submission is fraudulent. One or more intermediate signals may be automatically combined or aggregated to generate a detection conclusion for a submission. Once a fraudulent submission is detected, the present disclosure describes various ways to proceed (e.g., either automatically or manually), for example, the fraudulent submission may be ignored, or a person or account associated with the fraudulent submission may be penalized. The various descriptions provided herein should be read broadly to encompass various other services that accept user ratings and/or comments.