Social Network Scoring Algorithm for Spam Reduction
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Solution Overview
Problem
Social networks face challenges in personalizing member profiles and reducing noise from spam and irrelevant interactions, which affects user engagement and connectivity.
Innovation Solution
A social networking system that personalizes member profiles by determining relevant attributes based on viewer data, scores interactions, and suggests actions, using a scoring system to promote positive interactions and penalize spam, thereby enhancing user engagement and reducing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system suggests actions to members based on viewer and member data, then user engagement and connectivity are improved, but the system complexity and computational resources increase
Solution Approach 1:
The system segments the scoring process into distinct components: viewer score calculation, member score calculation, and suggested action scoring. Each component processes specific data types (viewer data, member data, interaction data) independently and combines results to generate the final suggested action score, reducing overall system complexity
Solution Approach 2:
The patent introduces intermediary scoring mechanisms that mediate between raw data inputs and final suggested actions. The viewer score and member score act as intermediaries that process and evaluate data before determining the final suggested action, simplifying the decision-making process
2Productivity
If the system personalizes member profiles by highlighting relevant attributes, then user engagement is improved, but the computational resources and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing viewer scores and member scores before they are needed for suggested action generation. This allows the system to quickly retrieve and combine pre-computed scores when generating suggested actions, reducing processing time at the point of user interaction
Solution Approach 2:
The patent applies local quality by personalizing profile attributes specifically for each viewer-member interaction pair rather than uniformly for all users. The system highlights attributes that are locally relevant to the specific interaction context, improving engagement without requiring complete profile personalization for all users
3Object-affected harmful factors
If the system scores and filters interactions to reduce spam, then noise reduction is achieved, but the system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where interaction outcomes are scored and used to refine future suggested actions. The scoring system provides feedback on the quality of interactions, allowing the system to learn from positive and negative outcomes and improve spam filtering over time without requiring complex manual intervention
Solution Approach 2:
The patent enables the system to self-service by automatically scoring and filtering interactions based on predefined criteria and algorithms. The system autonomously evaluates viewer scores, member scores, and interaction data to identify and reduce spam without requiring external moderation, simplifying the overall system architecture
Data Source
AI summary
Techniques for presenting a personalized member profile page to a viewer are described. The online social network service system can access a sender score of a sender requesting to view a profile page in an online social network service, and access a receiver score of a receiver associated with the profile page. Additionally, a suggested action can be determined based on the sender score and the receiver score, a sender confirmation to perform the suggested action can be received, and a communication associated with the suggested action can be transmitted in response to the received sender confirmation. Subsequently, the online social network service system can classify an interaction between the sender and the receiver, and update the receiver score and the sender score based on the classified interaction.