Dynamic Document Suggestion in Sparse Traffic Environments
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Solution Overview
Problem
Existing recommendation systems for users in high traffic/small product set scenarios, such as online shopping and content systems, are ineffective when user traffic is low or the number of potential items is large, particularly in corporate settings where user activities are sparse and document access is limited.
Innovation Solution
A computer system that models enterprise objects as nodes on a graph and relationships as edges, using a dynamic suggestion component to filter and rank documents based on conditional probability estimates, providing users with dynamically suggested related documents by analyzing user interactions and document usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing recommendation systems are used in high traffic/small product set scenarios, then recommendation accuracy is improved, but the system becomes ineffective when user traffic is low or the number of potential items is large
Solution Approach 1:
The patent implements dynamic recommendation approaches that adapt to varying traffic conditions and document sets. The system transitions from static, pre-computed recommendations to dynamic, real-time generation of recommendations based on current user interactions and document availability, enabling effective operation across both high-traffic and low-traffic scenarios
Solution Approach 2:
The system changes key parameters including traffic density thresholds, document set sizes, and interaction frequency metrics to determine the appropriate recommendation strategy. By monitoring these parameters, the system can switch between different recommendation algorithms optimized for specific conditions, maintaining accuracy across varying operational contexts
2Measurement precision
If traditional recommendation approaches are applied to sparse traffic environments with large document sets, then system complexity increases, but recommendation quality deteriorates
Solution Approach 1:
The patent segments the large document set into manageable subsets based on user interactions, document relationships, and relevance criteria. By dividing the vast corporate document repository into smaller, context-relevant groups, the system reduces computational complexity while maintaining recommendation quality through focused analysis of segmented document portions
Solution Approach 2:
The system performs partial analysis on the full document set by focusing computational resources on the most relevant document subsets identified through user interaction patterns and relationship graphs. Rather than analyzing all documents equally, the system applies excessive action only to high-priority segments, reducing overall complexity while preserving recommendation quality
3Measurement precision
If the system analyzes user interactions and document usage patterns to provide personalized recommendations, then recommendation relevance is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary indexing and pre-computation of document relationships, user profiles, and interaction patterns. By preparing recommendation data structures in advance and caching frequently accessed information, the system reduces real-time processing requirements while maintaining personalized recommendation relevance when users interact with documents
Data Source
AI summary
A computer system for dynamically surfacing related documents is provided. The computer system includes a processor that is a functional component of the computer system and is configured to execute instructions. The processor is operably coupled to a signal store having information indicative of a plurality of documents and relationships. A user interface component is coupled to the processor and is configured to receive a user selection of a first document. A dynamic suggestion component is configured to interact with the user interface component to receive an indication of the first document and access the signal store to provide a dynamic document suggestion based on relationships between other users and the plurality of documents in the signal store.