Social Network Group Recommendation System Using Affinity Scores
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Solution Overview
Problem
Social networking systems face inefficiencies in automatically organizing users into relevant groups, leading to resource-intensive and time-consuming manual processes that may result in users missing suitable groups and discouraging them from creating new groups due to the time required for user addition.
Innovation Solution
A social networking system provides group recommendations by identifying connected users and candidate groups based on shared characteristics, using affinity scores and activity levels to suggest groups for users to join or create, and automatically adding users to recommended groups, thereby simplifying the identification and creation of groups with similar interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual organization of groups by users is implemented, then users can identify relevant groups, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system pre-calculates and stores group compatibility scores based on user profiles, group characteristics, and connection data before users need to join groups. This preliminary processing enables rapid recommendation generation without requiring users to manually evaluate groups, thus reducing time loss while maintaining identification accuracy.
Solution Approach 2:
The patent introduces an automated recommendation system as an intermediary between users and groups. This intermediary analyzes user characteristics, group attributes, and connection data to generate ranked recommendations, eliminating the need for users to manually search and evaluate groups while ensuring accurate matching through algorithmic assessment.
2Productivity
If automated group organization is implemented, then the process is more efficient, but mistakes and irrelevant recommendations may occur
Solution Approach 1:
The system incorporates feedback mechanisms where user interactions with recommendations (acceptance, rejection, modifications) are continuously analyzed to refine recommendation algorithms. This feedback loop enables the system to learn from past performance, correct mistakes, and improve recommendation accuracy over time while maintaining high automation efficiency.
Solution Approach 2:
The recommendation system dynamically adjusts its parameters and weighting factors based on real-time data including user behavior patterns, group activity levels, and changing user preferences. This dynamic adaptation allows the automated system to maintain high reliability by responding to changing conditions rather than relying on static rules.
3Ease of operation
If users manually add users to groups, then group composition can be controlled, but the process is time-consuming and discourages group creation
Solution Approach 1:
The system pre-identifies and ranks potential group members based on user profiles, shared characteristics, and connection data before group creation or expansion. This preliminary identification provides administrators with pre-screened candidate lists, dramatically reducing the time required to add appropriate users while maintaining control over group composition through selective approval.
Solution Approach 2:
The patent implements self-service mechanisms where users can automatically join groups based on their profiles and preferences, or administrators can approve pre-selected candidates with a single action. This reduces the manual effort required for user addition from extensive searching and evaluation to simple confirmation, making group creation and expansion much easier while maintaining compositional control.
Data Source
AI summary
Based on information associated with users, a social networking system recommends one or more groups for a target user to join or to create. Characteristics of the target user, characteristics of users connected to the target user, characteristics of candidate groups in the social networking system may be used to identify groups for recommendation. The social networking system may provide questions to the target user and recommend a group to the target user based on received answers to the questions. For example, the answers to the provided question identify one or more characteristics of the target user, which are used to select a group for recommendation. Additionally, the social networking system may recommend additional users for the target user to add or invite to a group based on characteristics of the target user, the additional users, and/or the group.