Social Network Content Recommendation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional social networking systems fail to provide users with adequate access to relevant content from publishers, as they err on the side of caution to avoid spam, leading to users missing highly relevant content and publishers not sharing all their content due to concerns about overwhelming users.
Innovation Solution
A content recommendation system that creates objects associated with links to content items within a social networking system, determining an aggregate score based on popularity and user interest, and ranking these objects for potential presentation to users, ensuring that only the most relevant content is shown.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content sources deliver more content items to users, then user access to relevant content improves, but users may be overwhelmed with unwanted or spam content
Solution Approach 1:
The patent introduces an intermediary content recommendation system that sits between content sources and users. This system analyzes user profiles, interaction histories, and content characteristics to filter and rank content items, delivering only those most relevant to each user. The intermediary resolves the contradiction by preventing unwanted content from reaching users while still allowing comprehensive content distribution.
Solution Approach 2:
The system implements feedback mechanisms by analyzing user interactions (clicks, likes, shares, time spent) and using this information to continuously refine content recommendations. This feedback loop ensures that content delivery becomes increasingly accurate over time, maintaining high relevance while preventing spam from appearing in user feeds.
2Object-affected harmful factors
If content sources deliver less content items to users, then users are protected from spam, but users miss highly relevant content they would otherwise find desirable
Solution Approach 1:
The patent replaces manual content curation and simple filtering mechanisms with sophisticated machine learning algorithms. These algorithms automatically analyze content characteristics, user preferences, and interaction patterns to identify and deliver relevant content that would otherwise be missed, while simultaneously filtering out spam without human intervention.
Solution Approach 2:
The system dynamically adjusts recommendation parameters based on user behavior, content performance metrics, and contextual factors. By changing parameters such as relevance thresholds, diversity weights, and timing strategies, the system optimizes content delivery to maximize relevant content exposure while minimizing spam, adapting continuously to changing conditions.
3Productivity
If social networking systems present more content items, then user engagement increases, but system complexity in determining optimal content amounts increases
Solution Approach 1:
The patent segments the content recommendation system into distinct functional modules: content collection, user profiling, relevance scoring, ranking, and delivery. Each module handles specific tasks independently, making the overall complex system manageable and maintainable. This segmentation allows parallel processing and independent optimization of each component.
Solution Approach 2:
The system performs preliminary actions by pre-processing content items (extracting features, categorizing, initial filtering) and pre-building user profiles before actual content delivery. This preliminary work reduces the computational complexity during real-time recommendation generation, enabling high engagement rates without overwhelming system complexity during user interactions.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media configured to create objects associated with links to content items shared within a social networking system, the content items from content sources. Content sources having pages on the social networking system fanned by a user are determined. Objects associated with (links to) content items from the content sources having content source representations, such as pages, on the social networking system fanned by the user are collected. An aggregate score for a collected object associated with a link is determined based on popularity of a content item associated with the link within the social networking system and interest of the user in the content item. The collected object associated with the link is provided to be ranked for potential presentation of the link to the user based on satisfaction of a threshold.