Topic Extraction and User Matching for Content Sharing
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Solution Overview
Problem
Social networks often fail to effectively share digital content with users who are genuinely interested in the topic, as creators may not know or remember to share with interested users, leading to missed opportunities for engagement.
Innovation Solution
A system that extracts content topics and user topics based on activities, generates user scores, and creates a sorted list of users with matched topics, which can be boosted by social graph connections, to identify and rank interested users for content sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content creators manually share content with interested users, then personal connection and intent are maintained, but the system cannot scale to reach all potentially interested users
Solution Approach 1:
The patent introduces an intermediary system comprising topic extraction modules, user profile analyzers, and matching algorithms that act as a mediator between content creators and interested users. This automated intermediary analyzes user activities, extracts content topics, matches them with user interests, and generates sorted lists of interested users, thereby scaling the sharing process without losing track of potentially interested users
Solution Approach 2:
The system enables self-service by automatically analyzing user activities, extracting topics from content, and generating matched user lists without requiring manual intervention from content creators. The processor autonomously performs topic extraction, user interest analysis, and ranking operations, allowing the system to serve itself in identifying and connecting interested users
2Reliability
If the system analyzes user activities and generates sorted lists automatically, then reaching interested users improves, but the processing complexity and computational resources increase
Solution Approach 1:
The patent segments the complex processing task into distinct functional modules: topic extraction from content, user activity analysis, topic matching algorithms, and sorted list generation. Each module handles a specific aspect of the analysis, dividing the overall complexity into manageable segments that can be processed independently and efficiently
Solution Approach 2:
The system changes parameters by transforming unstructured user activity data into structured user profiles with identified interests, and transforming content into extracted topics. These parameter transformations enable efficient matching and sorting operations, reducing the complexity of subsequent processing steps
Data Source
AI summary
Examples of techniques for sharing content based on topics are described herein. A method includes extracting a content topic from a piece of content. The method includes extracting a user topic based on a user activity. The method also includes matching the content topic with the user topic and generating a user score based on a detected number activities a user performs that include a matched user topic. The method further includes generating a sorted list of users with matched user topics, the list to be sorted by the user score.