Automated Contact Aging and Relationship Classification
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Solution Overview
Problem
Existing social networking systems require users to manually manage connections, which is inconvenient and time-consuming, and users often fail to utilize list or group features due to complexity, leading to inactive relationships.
Innovation Solution
A system and method that classify relationships by scoring interactions based on group size and time since last interaction, assigning an expiration parameter to contacts, and modifying it based on detected interactions to automatically age and disassociate contacts, thereby maintaining relevant connections without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually manage connections by removing inactive contacts, then connection quality is improved, but user time and effort are consumed
Solution Approach 1:
The system automatically manages contact relationships by computing interaction scores and expiring contacts based on inactivity, eliminating the need for users to manually review and remove inactive connections. The patent implements this through automated scoring mechanisms that continuously evaluate contact activity and automatically expire contacts below threshold scores.
Solution Approach 2:
The system pre-assigns expiration parameters to contacts based on predicted future activity patterns and interaction history, allowing contacts to be automatically expired before users would need to manually manage them. This proactive approach prevents accumulation of inactive contacts without requiring user intervention.
2Reliability
If users specify lists or groups of important connections, then relationship management is improved, but system complexity increases
Solution Approach 1:
Instead of requiring users to manually create and maintain lists or groups, the system automatically computes interaction scores for all contacts and dynamically determines which contacts are important based on actual interaction patterns. This eliminates the need for users to understand or manage complex grouping structures.
Solution Approach 2:
The system uses interaction score thresholds as dynamic parameters to automatically classify and manage contacts. By changing the threshold parameter, the system can adaptively adjust which contacts are considered important without requiring users to reconfigure complex list structures or grouping hierarchies.
3Reliability
If users manually remove connections, then inactive contacts are eliminated, but user convenience deteriorates
Solution Approach 1:
The system performs automatic contact expiration based on computed interaction scores, completely eliminating the need for users to manually remove inactive contacts. This self-service mechanism maintains connection relevance while preserving user convenience by handling all management tasks automatically.
Solution Approach 2:
The system pre-computes interaction scores and assigns expiration parameters to contacts before users would need to take action. This allows the system to proactively eliminate irrelevant contacts while users continue to interact with the system normally without encountering manual management requirements.
4Measurement precision
If the system tracks and scores all interactions, then relationship classification accuracy is improved, but computational resources increase
Solution Approach 1:
The system applies different scoring weights to different types of interactions based on their local importance to relationship strength. Rather than uniformly processing all interactions with equal computational resources, the system assigns higher weights to more significant interaction types (e.g., direct messages, profile visits) and lower weights to less significant ones, optimizing computational efficiency while maintaining classification accuracy.
Solution Approach 2:
The system uses adjustable weighting parameters for different interaction types that can be tuned to balance accuracy and computational cost. By modifying these parameters, the system can adapt to different resource constraints while maintaining effective relationship classification through the interaction score computation.
Data Source
AI summary
One or more interactions between a first user and a second user of a social networking system are identified. Each respective interaction of the one or more interactions is scored based on a group score and a time penalty. The group score is based on the number of users in the respective interaction and the time penalty is based on a time between a current time and a time of a last interaction between the first user and the second user. A relationship ranking that measures the first user's affinity towards the second user is determined, where the relationship ranking comprises one or more interaction scores. An indicator representing the relationship ranking is sent to a client for display.