Real-Time Multi-Party Recommendation in Peer-to-Peer Media Transfer
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Solution Overview
Problem
Existing content transfer mechanisms between electronic devices do not provide for automatic recommendations based on user profiles, requiring manual intervention for users to recommend content.
Innovation Solution
A method and system for real-time multi-party recommendation in peer-to-peer communication, where electronic devices analyze metadata to recommend content automatically without user intervention, based on user profiles and shared content, enabling seamless exchange of electronic media between devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual content selection and recommendation is used, then users can control content transfer, but user intervention is required and content transfer efficiency is reduced
Solution Approach 1:
The system performs self-service by automatically analyzing metadata, generating recommendations, and facilitating content transfer without requiring manual user intervention. The electronic devices autonomously compare metadata, determine compatibility, and present recommendations to users, eliminating the need for manual content selection and significantly improving transfer efficiency.
Solution Approach 2:
The system performs preliminary actions by pre-analyzing metadata, pre-determining content compatibility, and pre-generating recommendations before actual content transfer. This preliminary processing enables faster decision-making and reduces the time required during the actual content transfer operation.
2Loss of information
If automatic recommendation based on user profiles is implemented, then content relevance is improved, but system complexity increases
Solution Approach 1:
The system extracts only the necessary metadata elements (content type, format, size, tags) from the full content data and user profiles. By taking out only the relevant features needed for recommendation rather than processing complete content or full profiles, the system achieves high content relevance while maintaining manageable complexity.
Solution Approach 2:
The system changes parameters by transforming complex content and profile data into standardized metadata parameters for comparison. This parameter transformation enables efficient automated recommendation by converting unstructured data into comparable formats, improving content relevance without proportionally increasing system complexity.
3Measurement precision
If metadata analysis is performed for recommendation, then recommendation accuracy is improved, but processing time increases
Solution Approach 1:
The system performs partial action by analyzing only the essential metadata fields required for recommendation rather than examining complete content data. This selective metadata analysis maintains sufficient recommendation accuracy while significantly reducing processing time compared to comprehensive content analysis.
Data Source
AI summary
The present invention relates to systems and methods for real-time multi-party recommendation in a peer to peer communication. The system (200) comprises a transceiver (202) that receives a selection for selecting a first electronic media from a first set of electronic media stored in the first user equipment (102), and transmit the same to the second user equipment (104). The system (200) further comprises a metadata generator unit (204) to generate first electronic media metadata; and a recommendation unit (206) to determine first recommendation metadata based on an analysis of first electronic media metadata and a first set of electronic media metadata. The transceiver (202) of the system (200) transmits the first electronic media metadata and the first recommendation metadata to the second user equipment (104), and receives, a second recommendation metadata based on an analysis of the first electronic media metadata and a second set of electronic media metadata.