Predictive Document Recommendation Using Correlation Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Document collaboration server systems face high computational loads due to numerous document search and access queries from interconnected documents, leading to increased latency and inefficiency in retrieving collaborative documents.
Innovation Solution
The system employs a three-layered recommendation approach using cross-document correlation models, cross-user correlation models, and user-document relationship sufficiency criteria to generate recommended document sets, reducing the need for extensive user queries by identifying relevant documents based on temporally correlated viewing sequences and qualifying user relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system provides comprehensive document search and access queries for interconnected collaborative documents, then document retrieval completeness is improved, but computational load and latency increase
Solution Approach 1:
The system pre-computes and stores document co-viewing relationships, user relationships, and document metadata in the cross-document correlation model and cross-user correlation model before queries are made. This preliminary action allows the system to quickly retrieve pre-processed information rather than computing relationships in real-time during document search operations, thereby maintaining retrieval completeness while reducing computational load and latency
Solution Approach 2:
The patent introduces predictive document recommendation as an intermediary layer between user document access requests and the actual document retrieval system. This intermediary uses the pre-computed correlation models to predict and pre-fetch documents that users are likely to access next, reducing the need for extensive real-time search queries and thereby decreasing computational load while maintaining document retrieval effectiveness
2Measurement precision
If the system processes numerous document search queries from users, then document access accuracy is improved, but server computational load increases
Solution Approach 1:
The system implements feedback mechanisms where user document viewing behavior is continuously monitored and fed back into the cross-document correlation model and cross-user correlation model. This feedback loop allows the models to learn from actual user interactions and improve their predictive accuracy over time, enabling the system to maintain high document access accuracy while reducing the number of queries needed by making smarter, more accurate predictions
Solution Approach 2:
The system pre-computes document co-viewing sequences, user relationship metrics, and document metadata in the correlation models before queries are made. This preliminary processing of document relationships and user behaviors allows the system to quickly determine accurate document recommendations without performing extensive real-time computations during user access requests, thereby maintaining accuracy while reducing server computational load
Data Source
AI summary
In general, embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive document recommendation in collaborative document server systems. In one example, a method comprises generating a recommended collaborative document set for a primary collaborative document using a cross-document correlation model that is characterized by a temporally correlated document viewing sequence set and a cross-user correlation model that is characterized by a qualifying user relationship set.