Messaging Search Work Graphs for Personalized Ranking
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Solution Overview
Problem
Current internet messaging systems lack efficient search and management capabilities, failing to effectively rank and retrieve relevant messages, channels, and users based on context and metadata, leading to suboptimal user experience and information retrieval.
Innovation Solution
The Messaging Search and Management (MSM) system generates and associates metadata with messages to create work graphs, utilizing machine learning structures for ranking and indexing, enabling the retrieval of relevant messages, channels, and users by capturing relationships between users, channels, topics, and topics, and providing personalized search results and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional messaging systems are used without advanced metadata analysis and machine learning, then the system complexity remains low, but the search accuracy and information retrieval effectiveness deteriorate
Solution Approach 1:
The system performs preliminary actions by generating and associating metadata with messages before search operations occur. Work graphs are constructed in advance by analyzing relationships between users, channels, topics, and messages, enabling rapid and accurate search retrieval without requiring complex real-time analysis during user queries.
Solution Approach 2:
The patent introduces work graphs as an intermediary structure that mediates between raw messaging data and search queries. These work graphs capture relationships and metadata in a structured format, allowing the system to achieve high search accuracy without directly implementing complex analysis algorithms during the search process itself.
2Productivity
If comprehensive metadata is generated and associated with all messages, then information retrieval effectiveness improves, but the processing time and computational resources increase
Solution Approach 1:
Metadata generation and work graph construction are performed as preliminary actions during message creation and storage, rather than during search operations. This allows the system to have comprehensive metadata ready for immediate retrieval, improving information effectiveness without adding processing delays during user interactions.
3Adaptability or versatility
If personalized search results and notifications are provided based on user behavior analysis, then user engagement improves, but the computational complexity and energy consumption increase
Solution Approach 1:
The system applies local quality by tailoring search results and notifications to individual user preferences and behaviors while maintaining a standardized underlying work graph structure. This allows personalization to be implemented selectively for different users without requiring complete system redesign, optimizing energy usage by applying complexity only where needed for personalization.
Data Source
AI summary
The Messaging Search and Management Apparatuses, Methods and Systems (“MSM”) transforms message, ranking request inputs via MSM components into work graphs, ML structure input data, ML structure, ranking response outputs. A work graph generation request that includes group level access control data may be obtained. A set of metadata access control carrying messages, a set of users, a set of channels, and a set of topics with access control data corresponding to the group level access control data may be determined. A user priority score for each of the other users, a channel priority score for each of the channels, and a topic priority score for each of the topics, from the perspective of each user, may be calculated. A work graph data structure may be generated that includes, for each user, data regarding the calculated user priority scores, channel priority scores, and topic priority scores.