Search Result Spam Profile Demotion via Trained Model
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Solution Overview
Problem
Social networking systems face challenges in presenting relevant user profile search results due to the presence of spam profiles, which hinder user experience and resource efficiency by requiring users to sift through irrelevant and inaccurate search results.
Innovation Solution
Implementing a system that demotes spam user profiles through a trained model, which identifies and filters out spam profiles from search results, using a combination of rule-based and machine-learned models to classify and adjust ranking scores, thereby improving the relevance and accuracy of search results presented to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all user profiles are included in search results, then the quantity of search results is maximized, but the quality and relevance of search results deteriorates due to spam profiles
Solution Approach 1:
The patent extracts and removes spam profiles from the search results using a trained machine learning model that identifies spam characteristics. The model scores each profile and removes those above a threshold, separating harmful content from useful results while maintaining the quantity of legitimate profiles.
Solution Approach 2:
The patent applies different quality standards to different profiles by using a spam detection model that evaluates each profile individually. Profiles are assigned local quality scores based on their specific characteristics, allowing the system to maintain high-quality results while removing only the spammy portions.
2Reliability
If spam profiles are removed from search results, then the quality of search results is improved, but the device complexity increases due to implementing spam detection models
Solution Approach 1:
The spam detection model operates autonomously, automatically scoring and removing spam profiles without requiring manual intervention. The system self-manages the filtering process, reducing the need for complex administrative oversight while maintaining high result quality.
Solution Approach 2:
The patent applies spam detection as a preliminary filtering step before presenting search results to users. By pre-processing profiles to remove spam before the user sees results, the system avoids the need for complex post-processing or user-side filtering mechanisms.
3Loss of information
If users are presented with more search results, then the completeness of search coverage is improved, but the time required to find relevant information increases
Solution Approach 1:
The patent converts the potential harm of including spam profiles into a benefit by using them as training data for the spam detection model. The model learns from spam characteristics to better identify and remove spam, turning the problem of spam volume into an opportunity to improve detection accuracy and maintain comprehensive yet clean results.
Data Source
AI summary
Techniques for improving a search user interface by demoting a set of spam user profiles included in a listing of candidate user profiles that are to be presented in response to a search request are disclosed herein. The search request is received from a client device. The search request requests the listing of candidate user profiles corresponding to a search query. The listing of candidate user profiles corresponding to the search query is determined. A trained spam detection model is applied to each of the candidate user profiles to identify the set of spam user profiles. The demoting of the spam user profiles is performed. The listing of candidate user profiles is communicated for presentation in the search user interface, the presentation reflecting the demoting of the spam user profiles.