Listing Legitimacy Detection via Term Distribution Analysis
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Solution Overview
Problem
Existing systems face challenges in distinguishing between legitimate and spam business listings, as spammers manipulate search rankings by submitting multiple titles and modifying listings to increase visibility, leading to fake or deceitful listings being ranked higher than genuine businesses.
Innovation Solution
A system and method that analyze the distribution of terms in listing titles to determine their similarity to natural language or uniform distributions, assigning a score value to assess the likelihood of spam, thereby adjusting the listing's position in search results based on this assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If spam users submit multiple listings and continuously modify them to increase ranking, then the listing visibility and ranking position improve, but the reliability of search results deteriorates
Solution Approach 1:
The system performs preliminary analysis of term distribution patterns in listings before ranking them in search results. By evaluating whether terms are distributed uniformly or follow natural language patterns in advance, the system identifies spam listings proactively and adjusts their ranking positions before users can be affected, thus maintaining search result reliability while allowing legitimate listings to rank properly
Solution Approach 2:
The system implements a feedback mechanism where the analyzed term distribution characteristics of listings are used to adjust their ranking positions. Listings with uniform term distribution (indicative of spam) receive penalty adjustments in their ranking, while listings with natural language term distribution patterns maintain or improve their ranking positions, creating a feedback loop that continuously improves search result quality
2Measurement precision
If the system analyzes term distribution patterns to identify spam listings, then the accuracy of spam detection improves, but the computational complexity increases
Solution Approach 1:
The system applies local quality analysis by examining the specific distribution pattern of terms within individual listings rather than analyzing entire databases uniformly. Each listing is evaluated based on its own term distribution characteristics (uniform vs. natural language patterns), allowing for precise local detection of spam without requiring complex global analysis, thus improving detection accuracy while managing computational complexity
Solution Approach 2:
The system changes the analysis parameter from examining individual terms to examining the distribution pattern of terms across listings. By focusing on whether term frequencies follow a uniform distribution or natural language patterns, the system transforms a potentially complex semantic analysis problem into a simpler statistical pattern recognition problem, improving detection accuracy while reducing computational complexity
Data Source
AI summary
A system and method is provided wherein, in one aspect, the likelihood of a listing being spam or enhanced by spam is determined based on whether the number of times that each term appears in descriptions of the listing tend to be equal or tend to reflect a natural language distribution. The likelihood that the listing is spam or enhanced by spam may be used to determine the position of the listing within search results to be displayed to a user.