Group Relevance System for Federated Application Token Suggestions
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Solution Overview
Problem
Existing automatic suggestion systems in computing systems often provide irrelevant suggestions to users as they are based on average relevance across all users, rather than the specific groups or associations relevant to the individual user, leading to inefficiencies in typing and information retrieval.
Innovation Solution
The system determines and suggests tokens relevant to the user's associated groups by partitioning user accounts based on their interactions with digital content, using techniques like Louvain Modularity, and ranks tokens based on their relevance within these groups, ensuring that suggestions are more pertinent to the user's context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automatic suggestion systems use average relevance across all users, then the system can provide suggestions for any user, but the suggestions become irrelevant to the specific user's context and needs
Solution Approach 1:
The system segments users into groups based on their account associations and interaction patterns with digital content. Instead of treating all users uniformly or requiring complex individual analysis, users are divided into meaningful groups where members share similar contexts and needs. This segmentation enables personalized suggestions through group-level analysis rather than individual-level complexity.
Solution Approach 2:
The patent introduces group accounts as intermediary entities between individual user accounts and the suggestion system. These group accounts aggregate interaction data from multiple users within a group, serving as a mediator that translates individual behaviors into group-level patterns. This intermediary layer simplifies the system architecture while enabling context-aware suggestions.
2Measurement precision
If the system analyzes individual user interactions in detail, then suggestion relevance improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system merges interaction data from multiple users within a group to create aggregated group-level interaction patterns. By combining data at the group level rather than processing each user's data separately, the system achieves precise suggestions through collective behavior analysis while reducing overall computational complexity. The merging of data streams enables pattern recognition that would be difficult to achieve through individual analysis alone.
Data Source
AI summary
Techniques are presented herein for improved automatic suggestions based on group relevance. The techniques include determining groups of account based on the interactions of those accounts with content items, e.g., on a federated system, and ranking tokens extracted from those content items based at least in part on one or more factors. When a partial input is received from a particular account, tokens are ranked based on groups associated with the account and are presented in the user interface.