Heterogeneous Network Search Module for Recommendation Accuracy
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Solution Overview
Problem
Current social network systems are limited in their ability to accurately recommend individuals for various tasks and purposes as they rely solely on information from a specific type of network, failing to leverage complementary information from heterogeneous networks.
Innovation Solution
The method involves accessing and combining multiple heterogeneous networks, including social and similarity networks, to generate a ranked list of recommendations based on the shortest connectivity degree, using computational analyses to identify individuals with similar skills or interests across different network types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If social network systems rely solely on information from a specific type of network, then the system complexity is reduced and ease of operation is improved, but the recommendation accuracy and comprehensiveness deteriorate
Solution Approach 1:
The patent combines multiple heterogeneous networks (social networks, professional networks, interest-based networks) into a unified recommendation system. The system integrates data from different network types and applies a common ranking algorithm to generate comprehensive recommendations, thereby improving recommendation accuracy without requiring separate systems for each network type.
Solution Approach 2:
The patent creates a universal recommendation system that can handle multiple types of networks simultaneously. The system uses a single framework that processes diverse network data (social connections, professional relationships, shared interests) through unified ranking algorithms, making the system multi-functional and adaptable to different network contexts.
2Measurement precision
If multiple heterogeneous networks are integrated, then recommendation comprehensiveness and accuracy are improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent pre-computes and stores ranking scores for network connections before queries are made. The system calculates initial rankings based on network data and stores these pre-computed values, allowing rapid retrieval and combination during recommendation generation, thus reducing real-time processing time while maintaining comprehensive analysis.
3Measurement precision
If multiple heterogeneous networks are integrated, then recommendation comprehensiveness is improved, but the data processing complexity and computational resources required increase
Solution Approach 1:
The patent transforms diverse network data into standardized parameters that can be processed uniformly. The system converts different network types (social, professional, interest-based) into common numerical representations and applies consistent ranking algorithms, simplifying the processing complexity while maintaining the benefits of multi-network integration.
Data Source
AI summary
At least one computer processor obtains access to a relationship network and a network of a different kind than the relationship network. The at least one computer processor also obtains a user query and carries out a multiple heterogeneous networks search on the relationship network and the network of the different kind than the relationship network, by executing on the at least one computer processor a multiple heterogeneous network search module, to obtain a ranked output list responsive to the user query.