Personalized Content Stream Generation via ML Scoring
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Solution Overview
Problem
Conventional social networking systems often result in users receiving a stale content feed due to following a limited number of users, leading to insufficient new content, which negatively affects user engagement.
Innovation Solution
A personalized content stream is generated using a machine learning model to score and rank content items based on user behavior and preferences, ensuring a continuous stream of relevant content, filtered to exclude inappropriate material and tailored to user interests, with content items clustered and ordered for improved consistency and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users follow a limited number of users, then the system is simple to operate, but the content feed becomes stale and lacks sufficient new content
Solution Approach 1:
The system automatically generates personalized content streams by analyzing user behavior patterns and preferences, eliminating the need for users to manually follow many accounts. The machine learning model self-adjusts content selection based on engagement metrics, providing continuous fresh content without user intervention.
Solution Approach 2:
The system changes the parameter of content selection from manual user-following to automated machine learning-based scoring. Content items are ranked and selected based on predicted engagement likelihood, transforming the content curation process from social graph-based to behavior-based parameter optimization.
2Device complexity
If users follow a limited number of users, then the system complexity is low, but user engagement is reduced due to insufficient content
Solution Approach 1:
The patent replaces the mechanical system of manual following/unfollowing with an automated machine learning model. The ML model processes user behavior data, content metadata, and interaction patterns to predict engagement likelihood, substituting complex manual curation with intelligent automation that maintains low system complexity while improving engagement reliability.
Solution Approach 2:
The system implements feedback loops where user interactions (likes, views, engagement duration) continuously refine the machine learning model's predictions. This feedback mechanism ensures the content stream adapts to changing user preferences, maintaining high engagement reliability without requiring complex manual adjustment.
3Ease of manufacture
If conventional content feeds are used, then the system is easy to implement, but content relevance to user interests is insufficient
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and content characteristics before content selection. The machine learning model pre-ranks content items based on predicted engagement likelihood, allowing the content feed to be pre-optimized for relevance before delivery, improving measurement precision without complicating implementation.
Solution Approach 2:
The patent changes the content selection parameters from binary follow/unfollow relationships to continuous engagement likelihood scores. This parameter transformation enables precise relevance measurement through probabilistic predictions while maintaining ease of implementation through automated scoring mechanisms.
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, wherein one or more of the candidate content items are to be included in a personalized content stream for a first user. A corresponding score for each of the candidate content items can be generated with respect to the first user. A first set of content items can be determined from the set of candidate content items based at least in part on the respective scores, wherein content items in the first set are included in the personalized content stream.