Semantic Network-Based Social Network Construction
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Solution Overview
Problem
Internet users face challenges in finding relevant information due to the abundance of irrelevant search results, leading to reliance on friends or acquaintances for advice, and existing social networks are typically built on a priori knowledge rather than shared interests.
Innovation Solution
A method and apparatus that monitor network usage to create semantic networks, identifying common interests by analyzing content relevance and building social networks by connecting users with shared interests through probable relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If social networks are built using a priori knowledge of nodes (such as adding existing friends or known organizations), then the social network structure is established quickly, but the network fails to capture actual shared interests and usage patterns of users
Solution Approach 1:
The system performs preliminary analysis of user network usage patterns, searched terms, and content interactions to pre-identify shared interests and probable relationships. This preliminary action enables the system to automatically build accurate social networks without requiring manual node addition, thus resolving the contradiction between quick network establishment and accurate interest capture.
Solution Approach 2:
The system enables social networks to self-build by automatically analyzing user behavior data and identifying relationships based on shared interests. Instead of requiring manual input from users to define their networks, the system autonomously constructs social networks by monitoring and interpreting user usage patterns, thereby capturing authentic shared interests while establishing networks efficiently.
2Measurement precision
If users rely on friends or acquaintances for advice before searching, then the relevance of information retrieval is improved, but the time and effort required to identify appropriate contacts increases
Solution Approach 1:
The system uses feedback from user search behavior, content interactions, and network usage patterns to continuously refine and update social network connections. By monitoring what users search for and how they interact with content, the system dynamically adjusts the relevance of recommended contacts, ensuring that users are connected to the most appropriate individuals for their current information needs without manual intervention.
Solution Approach 2:
The system pre-identifies and establishes connections between users with shared interests before users need to search for information. By proactively building social networks based on analyzed usage patterns, the system ensures that relevant contacts are already identified and available when users need advice, eliminating the time required to manually search for appropriate contacts while maintaining high information relevance.
3Measurement precision
If semantic networks are created based on monitored network usage, then the accuracy of identifying common interests is improved, but the complexity of the system increases
Solution Approach 1:
The system employs a multi-functional analysis engine that simultaneously monitors network usage, analyzes searched terms, processes content interactions, and identifies shared interests through a unified semantic network framework. This universal approach consolidates multiple functions into a single system architecture, improving the accuracy of interest identification while managing system complexity through integrated rather than separate processing components.
Data Source
AI summary
A method, apparatus and program product are provided for identifying common interests between users of a communication network. A program of instruction monitors activity over a communication network by users and identifies interests for users based on network activity. The program of instruction creates semantic networks based on use of the communication network and identifies other users with common interests from the semantic networks. Optionally, social networks may be created or modified by adding other users with common interests as identified by semantic networks.