Intelligent Resource Ranking via Intention Inference
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information systems require users to manually navigate hierarchical folder structures or apply tags to locate resources, making it inefficient to quickly and accurately find the intended resources, and they lack the ability to infer user intentions based on context for personalized recommendations.
Innovation Solution
A computer system with an intention inference engine that predicts user intentions by generating weighted expressions from contextual information and a user model, and an intelligent ranking engine that sorts resources based on these expressions, allowing for proactive and personalized resource recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually navigate hierarchical folder structures to locate resources, then the system maintains a simple storage structure, but the time required to locate resources increases significantly
Solution Approach 1:
The system automatically infers user intentions and ranks resources without requiring manual navigation. The intention inference engine analyzes user context, behavior patterns, and system state to autonomously determine what resources the user needs, eliminating the need for users to manually explore folder structures.
Solution Approach 2:
The patent replaces the mechanical interaction of manual folder navigation with an intelligent software-based intention inference system. Instead of users physically navigating through hierarchical structures, the system uses machine learning models to predict user needs and automatically retrieve relevant resources.
2Loss of time
If users manually apply tags to documents for filtering, then the system maintains simple resource organization, but the effort and time required for resource location increases
Solution Approach 1:
The system automatically infers user intentions without requiring manual tagging. The intention inference engine analyzes contextual information, user behavior patterns, and resource metadata to automatically determine relevant resources, replacing the manual tagging process with autonomous intelligent inference.
Solution Approach 2:
The system performs preliminary analysis of user needs and resource relevance before the user actually searches for resources. By continuously monitoring user context and system state, the system prepares and ranks potential resources in advance, so when a user needs something, the relevant resources are already ready and ranked.
3Adaptability or versatility
If traditional recommender systems infer similarities across users, then the system can provide general recommendations, but it cannot provide personalized recommendations based on individual user private resource spaces
Solution Approach 1:
The system applies local quality by creating personalized user models for each individual user based on their private resource space and behavior patterns. Instead of treating all users uniformly, the intention inference engine adapts its inference to each user's specific context, preferences, and historical interactions, providing locally optimized recommendations for each user.
Solution Approach 2:
The system uses dynamic user models that continuously adapt and evolve based on user interactions and changing context. The user models are not static but are continuously updated as the system learns from user behavior, allowing the recommendations to dynamically adjust to user needs and preferences over time.
4Device complexity
If the system requires users to create hierarchical folder trees, then the system maintains simple resource storage structure, but the complexity of resource organization increases
Solution Approach 1:
The system eliminates the need for users to manually create and maintain hierarchical folder structures. The intelligent ranking system automatically organizes and ranks resources based on inferred user intentions, replacing manual organization efforts with autonomous system-based organization.
Solution Approach 2:
Instead of organizing resources first and then having users search through them, the system inverts the approach by first inferring user intentions and then retrieving and ranking relevant resources. This inversion eliminates the need for pre-established hierarchical organization structures.
Data Source
AI summary
A computer-implemented machine-learning method and system for searching for resources by predicting an intention and pushing resources directly to users based on the predicted intention. The method includes receiving a description of a context; generating a set of weighted expressions, each weighed expression comprising a restriction over the description of the context and a confidence factor resulting between the combination of the user model and of the query input; and generating a sorted list of resources matching the weighted list of expressions. The system includes computer instructions for an intention inference engine and an intelligent ranking engine.