Fused Relationship Network for Cross-Platform User Recommendation
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Solution Overview
Problem
Current user recommendation systems face inefficiencies and inaccuracies due to the limited scope of user relationship networks on single platforms, which fail to capture diverse user needs and interactions across multiple platforms.
Innovation Solution
A method and apparatus for recommending information based on a fused relationship network, where nodes represent users, edges contain interaction information with weights determined by interaction and data source information, allowing for the determination of association relationships and targeted information recommendations across multiple platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If user recommendation is based on single-platform operation data, then the system is simple to implement, but the recommendation accuracy and efficiency deteriorate due to limited scope of user relationships
Solution Approach 1:
The patent merges user relationship networks from multiple platforms into a unified fused relationship network. This combines data from different sources to create a comprehensive view of user relationships, thereby improving recommendation accuracy without requiring complete reconstruction of the system architecture.
Solution Approach 2:
The fused relationship network serves multiple functions: it captures user relationships across different platforms, enables diverse recommendation scenarios, and adapts to various data types. This multi-functionality allows a single system to handle complex cross-platform recommendations while maintaining operational simplicity.
2Adaptability or versatility
If user relationship network expands to multiple platforms, then the coverage of user relationships improves, but the complexity of data processing and network management increases
Solution Approach 1:
The patent segments the fused relationship network into platform-specific sub-networks while maintaining a unified overall structure. This segmentation allows independent processing of each platform's data, reducing the complexity of managing multi-platform relationships while preserving comprehensive user relationship coverage.
Solution Approach 2:
The patent introduces a fused relationship network as an intermediary layer between multiple platform data sources and the recommendation engine. This intermediary abstracts and standardizes diverse platform data, simplifying the processing complexity while expanding user relationship coverage across platforms.
3Measurement precision
If edge weights are determined based on both interaction information and data source information, then the precision of association relationships improves, but the computational requirements increase
Solution Approach 1:
The patent changes the parameters used for edge weight determination to include both interaction information and data source information. This multi-parameter approach improves association relationship precision by considering multiple factors, while the weighted fusion mechanism manages computational requirements through efficient parameter integration.
Data Source
AI summary
The present disclosure provides a method and an apparatus of recommending information based on a fused relationship network. The method includes: determining association relationships between an any node and other nodes in the fused relationship network based on at least one of a weight, interaction information and data source information of the interaction information of an edge; and recommending information to a user represented by the any node based on the association relationships. The present disclosure further provides an apparatus of recommending information based on a fused relationship network, and an electronic device and a storage medium.