Social Network Page Recommendation via Site Decay Scoring
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Solution Overview
Problem
Conventional approaches to recommending content in social networks fail to effectively identify and provide high-interest content to users due to the sheer volume of available content, leading to a decline in user engagement as the size of social networks grows.
Innovation Solution
The system analyzes user interactions and site visits to generate page recommendations by determining frequently visited sites, calculating decayed visit scores, and using machine learning models to predict user interest, while also employing collaborative filtering and clustering to map user interests to relevant pages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the size of social networks grows, then the amount of available content increases, but the ability to identify high-interest content deteriorates
Solution Approach 1:
The patent segments the vast content universe into manageable units by analyzing user interactions at the page level rather than individual content items. It divides users into segments based on their interaction patterns with external websites, and segments content by website categories. This segmentation makes the large-scale content identification problem tractable by working with aggregated page-level signals rather than individual content pieces.
Solution Approach 2:
The patent introduces page recommendations as an intermediary mechanism between users and content. Instead of directly analyzing individual content items across the entire network, the system uses pages as intermediate entities that aggregate content from external websites. These pages serve as mediators that translate user browsing behavior into personalized content recommendations, bridging the gap between user interests and available content.
2Ease of operation
If conventional approaches provide additional content items based on user interest, then user experience may be enhanced, but user engagement often fails to maintain high levels
Solution Approach 1:
The patent implements feedback mechanisms by tracking user interactions with recommended pages and using this information to refine future recommendations. The system monitors which pages users click on, spend time on, and interact with, then feeds this behavior data back into the recommendation engine. This continuous feedback loop allows the system to adapt to changing user interests and maintain engagement over time, rather than providing static recommendations.
Solution Approach 2:
The patent performs preliminary actions by proactively analyzing user browsing behavior and pre-computing page recommendations before users actively search for content. The system monitors user interactions with external websites in real-time, pre-processes this data to identify interest patterns, and has recommendations ready when users return to the platform. This preliminary preparation ensures recommendations are timely and relevant, improving engagement.
3Quantity of substance
If the volume of content increases, then theoretically more high-interest content should be available, but the complexity of identifying such content increases
Solution Approach 1:
The patent creates simplified copies of the complex content identification problem by working with page-level representations rather than individual content items. Instead of analyzing the full complexity of each content item across millions of pages, the system creates aggregated page profiles that capture essential characteristics. These page copies serve as proxies that reduce the computational complexity while preserving the essential information needed for recommendation.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine at least one web site that is of interest to a user of the social networking system. One or more pages can be determined based at least in part on the web site, the one or more pages being accessible through the social networking system. At least one page recommendation that references at least one of the one or more pages can be provided to the user.