Group Engagement Recommendations via Graph Mapping
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Solution Overview
Problem
Existing communication systems fail to effectively recommend engagement between organizational groups within a cloud-based communication platform, as they primarily focus on user-to-user or user-to-group connections, neglecting the potential utility of group-to-group interactions and lacking the ability to analyze complex communication patterns across the organization.
Innovation Solution
An application server analyzes user communication connections in a graphical data structure, mapping these connections against the organizational structure to generate recommendations for engagement between groups based on connection strengths, which are weighted by factors like communication channel usage and time since last interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the system focuses on user-to-user or user-to-group connections, then the implementation is simpler, but the ability to recommend group-to-group interactions is lost
Solution Approach 1:
The system segments the recommendation task into two levels: individual user connection analysis and organizational group connection synthesis. By dividing the complex group-to-group recommendation problem into smaller user-level connection components, the system maintains implementation simplicity while enabling sophisticated group-level insights through aggregation of individual connection patterns
Solution Approach 2:
The system transitions from analyzing only user-to-user or user-to-group connections (single dimension) to incorporating organizational group mappings as an additional dimension. This allows the system to project individual connection strengths onto group-level relationships, enabling group-to-group recommendations without significantly increasing implementation complexity
2Measurement precision
If the system analyzes all communication patterns across the organization, then the recommendation accuracy improves, but the computational complexity increases
Solution Approach 1:
The system extracts only the essential connection information from communication patterns - specifically, the strength of connections between individual users and their organizational group memberships. By taking out only these critical elements rather than analyzing all communication metadata, the system achieves good recommendation accuracy while keeping computational complexity manageable
Solution Approach 2:
The system performs partial analysis by focusing on connection strengths and organizational mappings rather than examining every aspect of communication patterns. This selective approach provides sufficient accuracy for collaboration recommendations without the computational burden of complete communication pattern analysis
3Reliability
If the system weights connections by multiple factors, then the recommendation quality improves, but the analysis complexity increases
Solution Approach 1:
The system applies different weighting factors to different aspects of connections locally - using communication frequency, channel types, and recency metrics specific to each connection context. By applying appropriate weights locally rather than using a single global weighting scheme, the system improves recommendation quality while keeping the analysis framework relatively simple and modular
Data Source
AI summary
Methods, systems, apparatuses, devices, and computer program products are described. A server may retrieve connection information of users of a group-based communication platform corresponding to inclusion of individual users in one or more communication channels. The server may retrieve membership information of the users corresponding to inclusion of the users in one or more groups associated with an entity. The server may generate a graphical data structure including one or more user nodes, one or more communication channel nodes, one or more group nodes, one or more edges corresponding to the connection information, one or more edges corresponding to the membership information, or any combination thereof. The server may generate an engagement recommendation for users included in a first group to engage with users of a second group of the one or more groups.