Implicit Social Graph for Dynamic Contact Suggestions
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Solution Overview
Problem
Users do not often create and maintain custom contact groups due to the time-consuming and tedious process, leading to static groups that become stale and lose utility in suggesting contacts for draft messages, especially in dynamic communication environments.
Innovation Solution
The introduction of an implicit social graph that uses interaction frequency, recency, and direction to suggest contacts by identifying meaningful clusters without considering content, allowing for dynamic and accurate contact suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If users manually create and maintain custom contact groups, then contact organization is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system automatically generates contact suggestions by analyzing interaction patterns in the implicit social graph without requiring manual user intervention. The algorithm self-updates contact groupings based on observed communication behaviors, eliminating the need for users to manually create and maintain contact groups while still providing organized contact recommendations.
Solution Approach 2:
The implicit social graph dynamically updates contact suggestions based on real-time interaction data. As users communicate with different contacts, the system automatically adjusts contact groupings and suggestions, ensuring the contact organization remains current without manual intervention. This dynamic adaptation resolves the contradiction by making the system responsive to changing communication patterns automatically.
2Stability of the object's composition
If static custom contact groups are used, then contact organization is achieved, but relevance is lost over time as groups become stale
Solution Approach 1:
The system replaces static contact groups with a dynamic implicit social graph that continuously adapts to changing communication patterns. Contact suggestions are automatically updated based on recent interaction frequency, ensuring relevance in dynamic environments while maintaining organized group structures through algorithmic clustering of contacts with similar interaction patterns.
Solution Approach 2:
The system uses interaction data as feedback to continuously refine contact suggestions. By monitoring communication patterns between users and contacts, the implicit social graph automatically adjusts contact groupings and recommendations, ensuring that contact organization remains relevant to current communication needs without manual intervention.
3Stability of the object's composition
If manual contact group creation is required, then contact organization is improved, but ease of operation deteriorates
Solution Approach 1:
The system performs contact organization automatically through the implicit social graph algorithm, which analyzes interaction patterns and generates contact suggestions without user intervention. This eliminates the operational burden of manually creating and maintaining contact groups while still providing structured contact organization based on actual communication behaviors.
Solution Approach 2:
The contact organization system dynamically adapts to user behavior automatically, updating contact suggestions as communication patterns change. This dynamic self-adjustment maintains effective contact grouping without requiring ongoing user effort, resolving the contradiction between organized contact management and ease of operation.
4Quantity of substance
If friend-of-friend ties are considered in the social graph, then network coverage is expanded, but privacy is compromised
Solution Approach 1:
The system segments the social graph into private first-degree connections (direct contacts) and public second-degree connections (friends of friends). Contact suggestions are generated based on private interaction data only, while public information from the broader network provides contextual enrichment without exposing private communication patterns. This segmentation allows expanded network coverage while protecting user privacy.
Data Source
AI summary
Systems and method for generating contact suggestions are provided in which one or more suggested contacts are identified from a set of contacts associated with a user. This includes identifying in historical communications in a user account associated with the user, one or more groups of contacts. Scores are generated for contacts in the identified groups of contacts, including generating a contact score for a respective contact by accumulating score contributions for a plurality of the identified groups of contacts that include the respective contact. One or more suggested contacts are identified in accordance with the generated scores. A contact suggestion is sent based on the suggested contacts for display to the user. The contact suggestion includes a suggested contact with a generated score that is above a predefined threshold.