Spectral Clustering for Local Content Suggestions
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Solution Overview
Problem
Conventional approaches to recommending content items in social networks often fail to reliably identify quality content of interest to users, especially as membership size and content availability increase, leading to a suboptimal user experience.
Innovation Solution
The implementation of a spectral clustering technique to generate geographic sub-regions and the application of a tf-idf technique to determine relevant pages for users, where pages are filtered based on locality conditions to provide personalized content suggestions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional recommendation approaches are used in social networks, then content items can be provided to users, but the reliability of identifying quality content of interest to users deteriorates as membership size and content availability increase
Solution Approach 1:
The patent segments the user base and content into geographic regions and local communities. By dividing the large-scale social network into smaller geographic segments (cities, metropolitan areas, regions), the system can apply localized recommendation strategies that maintain reliability even as overall network size grows. This segmentation allows recommendations to be contextually relevant to each geographic segment rather than diluted across the entire network.
Solution Approach 2:
The patent implements local quality by tailoring recommendation content to specific geographic locations. Different geographic regions receive different recommended content based on local user preferences, behaviors, and characteristics. This ensures that content quality and relevance are optimized for each local market rather than applying a uniform approach across all users, thereby maintaining identification accuracy as the network expands.
2Measurement precision
If spectral clustering and tf-idf techniques are applied to generate geographic clusters and determine relevant pages, then recommendation accuracy is improved, but system complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing geographic clusters using spectral clustering and pre-calculating page relevance scores using tf-idf techniques. These computations are performed in advance to create geographic region definitions and page importance rankings, which are then reused for making recommendations. This approach improves recommendation accuracy while managing system complexity by avoiding repeated complex calculations for each recommendation query.
Solution Approach 2:
The patent introduces intermediary structures including geographic region definitions, similarity graphs, and pre-computed page scores as mediators between raw user data and final recommendations. These intermediaries simplify the recommendation process by providing structured, pre-processed information that reduces the computational complexity of real-time recommendation generation while maintaining high accuracy.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media are configured to apply a spectral clustering technique to at least a portion of a similarity graph to generate clusters of geographic sub-regions constituting geographic regions. A tf-idf technique is performed to determine pages of a social networking system associated with a geographic region as potential local suggestions for a user associated with a geographic sub-region in the geographic region. References to at least a portion of the pages are presented as local suggestions to the user.