Content Recommendation System Using Bid-Based Auction Segmentation
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Solution Overview
Problem
Conventional social networking systems fail to effectively recommend content items of high interest to users due to the sheer volume of content, often presenting irrelevant or unfamiliar content despite user interest indications.
Innovation Solution
The system identifies seed content items based on user interest and generates candidate content items using machine learning models to predict interaction probabilities, assigning bid values for optimal presentation through an auction system, considering language match and quality, and providing explanations for content recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the social network attempts to provide additional content items based on user interest, then user experience and engagement are enhanced, but the ability to identify high-interest content deteriorates due to the sheer volume of content
Solution Approach 1:
The patent segments the content recommendation process into multiple stages: generating candidate content items from seed content, scoring candidates based on user interaction history, and selecting final recommendations. This segmentation allows the system to manage content volume by processing content in manageable stages rather than evaluating all content simultaneously, thereby maintaining identification accuracy despite large content volumes.
Solution Approach 2:
The patent applies local quality by creating user-specific recommendation lists tailored to individual user interests and interaction patterns. Instead of applying a uniform content selection approach across all users, the system customizes content scoring and selection for each user based on their specific interaction history with content items, thereby improving recommendation effectiveness while managing content volume through personalized filtering.
2Productivity
If conventional systems present content items to users, then content delivery is achieved, but the content is often irrelevant or unfamiliar to users
Solution Approach 1:
The patent implements preliminary action by pre-scoring and pre-filtering content items based on user interaction history before presenting them to users. The system analyzes user interactions with seed content items in advance, generates candidate content items, and scores them according to predicted user interest. This preliminary processing ensures that only relevant content reaches the user, improving content relevance while maintaining delivery efficiency through automated pre-screening.
Solution Approach 2:
The patent employs feedback mechanisms by continuously analyzing user interactions with content items and using this information to refine future content recommendations. The system monitors user behavior patterns, adjusts content scoring based on actual user responses, and iteratively improves recommendation accuracy. This feedback loop ensures content relevance by learning from user preferences over time while maintaining efficient content delivery through data-driven selection.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media configured to determine seed content items based on interests of a user. Candidate content items can be determined for potential presentation to the user based at least in part on the seed content items. Features associated with the candidate content items can be processed to generate probabilities that the user will perform interactions with the candidate content items. Values can be assigned to the candidate content items based on the probabilities that the user will perform interactions with the candidate content items and the importance of the interactions. The values can be provided as bid values to an auction system to determine constraints regarding presentation of the candidate content items. Presentation of the candidate content items can be optimized.