Event Metadata Content Recommendation System
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Solution Overview
Problem
Users face difficulties in efficiently searching and retrieving specific multimedia content from large volumes uploaded to social network services, requiring a method to automatically recommend content associated with a particular event without manual browsing through multiple online service addresses.
Innovation Solution
A method and system that generate event metadata from user-input information, search for and recommend content on a social network service server based on this metadata, and provide the recommended content using calculated recommendation values derived from social metadata, including popularity, social relation, and content similarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually browse and select files from multiple online service addresses to create albums, then they can find specific content, but the process takes a long time and is not easy
Solution Approach 1:
The system automatically generates event metadata from user input and performs content search and recommendation without requiring manual browsing. The algorithm autonomously processes metadata generation, content matching, and result ranking, enabling the system to serve itself in the content retrieval process.
Solution Approach 2:
Metadata serves as an intermediary between user search intent and actual content. Instead of directly browsing files, users input event information that is transformed into metadata, which then mediates the search process to automatically retrieve relevant content from multiple online services.
2Adaptability or versatility
If users directly search for content associated with a particular event by visiting each online service, then they can find relevant content, but the operation becomes complex and time-consuming
Solution Approach 1:
The system provides a universal search interface that handles multiple online services through a single event metadata input. Instead of requiring separate operations for each service, the unified system performs content search across Facebook, Instagram, Google+, and other platforms through one standardized process.
Solution Approach 2:
The system extracts essential event information from user input and encapsulates it into structured event metadata. This extracted metadata contains only the necessary elements (event name, date, location, participants) needed for content search, separating essential search criteria from unnecessary complexity.
3Quantity of substance
If the system searches through large amounts of uploaded content, then it can find relevant content, but the search process becomes inefficient
Solution Approach 1:
The system extracts and utilizes pre-existing metadata from uploaded content (such as event names, dates, locations, and participant information) to create search indexes. This extracted metadata enables efficient filtering and matching without requiring full content analysis, significantly improving search speed across large content volumes.
Solution Approach 2:
The system transforms unstructured or semi-structured content metadata into standardized event metadata parameters that can be efficiently compared and matched. By changing the parameter representation of content attributes, the system enables rapid filtering and sorting of large content sets based on event characteristics.
Data Source
AI summary
A method, apparatus, and system are configured to recommend content using metadata including an event metadata unit, a searching unit, and a control unit. The event metadata generating unit is configured to generate event metadata regarding content selected by a user. The searching unit which, on the basis of the generated event metadata and social metadata regarding content uploaded to an SNS server associated with the user, is configured to search the content uploaded to the SNS server to recommend content to be provided to the user. The control unit is configured to provide the recommended content to the user using the social metadata regarding the searched recommended content.