Social Affinity Coefficients for Content Ranking
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Solution Overview
Problem
Conventional approaches to ranking content items in social networking systems do not effectively consider the strength of user relationships, leading to inefficiencies in prioritizing content that is likely to be of interest to users.
Innovation Solution
The use of social affinity coefficients measured across multiple social networking systems to rank content items, where these coefficients quantify the strength of relationships between users, and a machine learning model is trained to predict user interactions based on these coefficients, ensuring that content items are prioritized in content feeds based on user affinity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional approaches are used to rank content items, then the content feed includes content from followed users, but the content items are not effectively prioritized based on user relationship strength
Solution Approach 1:
The patent introduces social affinity coefficients as a new parameter to quantify the strength of relationships between users. This parameter is calculated based on various factors including interaction frequency, reciprocal following, and engagement metrics. By incorporating this additional parameter, the system transforms the content ranking process from a simple presence/absence model to a nuanced scoring system that reflects actual relationship dynamics, thereby improving content ranking accuracy without excessive complexity
Solution Approach 2:
The patent replaces conventional mechanical ranking approaches with machine learning models that automatically learn and apply social affinity patterns. The system uses trained models to predict user engagement likelihood based on social affinity coefficients, substituting manual or rule-based ranking mechanics with intelligent algorithms that adapt to user behavior patterns, improving precision while managing system complexity through automated learning
2Productivity
If social affinity coefficients are used to prioritize content items, then user engagement and relevance are enhanced, but computational resources and processing time increase
Solution Approach 1:
The patent pre-calculates and stores social affinity coefficients between user pairs in advance, rather than computing them in real-time when generating content feeds. The system maintains updated affinity matrices that capture relationship strengths, allowing rapid content scoring during feed generation. This preliminary computation approach shifts the computational burden to off-peak times and enables efficient real-time content prioritization with minimal energy consumption during user interactions
Solution Approach 2:
The patent implements a two-stage filtering approach where social affinity coefficients are used to prioritize candidate content items, but not all possible content items are scored. The system first identifies a subset of relevant content based on basic criteria, then applies the computationally intensive social affinity scoring only to this reduced set. This partial application of the complex scoring mechanism achieves high user engagement while significantly reducing overall computational energy requirements
3Measurement precision
If multiple social networking systems are integrated to measure social affinity, then the accuracy of relationship measurement improves, but system complexity and data integration challenges increase
Solution Approach 1:
The patent introduces a centralized social affinity calculation service that acts as an intermediary between multiple social networking systems. This service receives raw interaction data from various platforms, standardizes the information, and computes unified social affinity coefficients. The intermediary layer abstracts the complexity of multi-system integration, allowing accurate cross-platform relationship measurement while isolating the core ranking system from integration challenges and enabling standardized data processing across diverse sources
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can generate a set of candidate content items from a plurality of content items that are available in the social networking system for a first user. A corresponding score for each of the candidate content items can be generated based at least in part on one or more social affinity coefficients corresponding to the first user and a respective second user associated with a candidate content item, wherein a social affinity coefficient provides a quantitative measurement of the strength of a relationship between two users. A first set of content items from the set of candidate content items can be determined based at least in part on the respective scores, wherein content items in the first set are included in a content feed provided to the first user.