Document Suggestion System for Virtual Spaces
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Solution Overview
Problem
Users face inefficiencies in finding and accessing relevant documents within communication platforms, leading to increased search time and resource usage, as existing systems lack effective mechanisms for surfacing relevant documents in virtual spaces.
Innovation Solution
The implementation of a feature that suggests relevant documents based on user and virtual space attributes, allowing users to invoke a list of suggested documents through keyboard shortcuts or overflow menus, which includes permissions management to ensure appropriate access levels, thereby enhancing collaboration and reducing search efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually search for relevant documents in communication platforms, then they can find documents, but search time and resource usage increase significantly
Solution Approach 1:
The system pre-processes and analyzes document metadata, user profiles, and virtual space attributes beforehand to prepare recommendation data. When a user needs documents, the system instantly retrieves pre-analyzed results rather than performing searches in real-time, thereby reducing search time while maintaining high document access efficiency
Solution Approach 2:
The system creates and maintains copies of document metadata, tags, and attribute information in optimized data structures that can be quickly queried. Instead of searching through original large-scale document repositories, the system queries pre-processed copies containing extracted features and relationships, enabling fast document retrieval without compromising access efficiency
2Loss of information
If the system surfaces more documents to users, then document relevance improves, but computing resource usage increases
Solution Approach 1:
The system applies different processing depths and analysis levels to different documents based on their relevance scores and user context. High-priority documents receive more thorough analysis while lower-priority ones use lighter processing, ensuring document relevance is maintained for important items while reducing overall computing resource consumption across the system
Solution Approach 2:
The system dynamically adjusts recommendation parameters such as the number of documents surfaced, analysis depth, and filtering criteria based on user context, virtual space importance, and system load conditions. This allows the system to maintain high document relevance when needed while reducing computing resource usage during low-priority operations or system constraints
3Reliability
If the system implements comprehensive permissions management for suggested documents, then access security improves, but system complexity increases
Solution Approach 1:
The system pre-establishes permission templates and access rules for different virtual spaces and document types before users interact with documents. Permission checks are pre-computed based on user roles and document metadata, allowing the system to maintain strong access security while avoiding complex real-time permission evaluation logic during document suggestions and access operations
Data Source
AI summary
In association with a communication platform, one or more users can create, share, edit, and/or comment on a document. Some examples of this disclosure are related to providing a list of suggested documents that a user can reference (e.g., add a link to) in a virtual space (e.g., in a message or post to one or more other users). For example, a user can be interacting with a virtual space (e.g., composing a direct message, a channel post, a thread, a workspace, a document, and the like) and invoke a list of suggested documents that can be referenced in the virtual space. In examples of the present disclosure, the list of suggested documents can include documents that are identified (e.g., based on one or more conditions being met) to be relevant to, or otherwise associated with, the virtual space.