Subset Multi-Objective Optimization for Social Network Revenue
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Solution Overview
Problem
Existing multi-objective optimization techniques for social networks are computationally inefficient and can negatively impact user experience when applied to large user bases, as they often require direct modeling of the entire network, which is impractical and may not be statistically representative.
Innovation Solution
The approach involves selecting a statistically relevant subset of users to determine optimal configurations for revenue generation while maintaining user engagement, using a multi-objective optimization module that adjusts subset size based on the number of objectives and desired statistical confidence, allowing for testing and refinement of these configurations before broader implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-objective optimization is applied to the entire social network, then optimization accuracy is improved, but computational burden increases significantly
Solution Approach 1:
The patent divides the entire social network user base into multiple subsets based on user characteristics, behavior patterns, and demographic factors. Each subset is optimized separately through multi-objective optimization algorithms, allowing the system to achieve comprehensive coverage without the prohibitive computational cost of optimizing the entire network at once. This segmentation enables parallel processing and reduces the complexity of the optimization problem.
Solution Approach 2:
The patent applies multi-objective optimization to a carefully selected subset of users that is statistically representative of the entire network, rather than applying it to all users. This partial action approach maintains optimization accuracy by focusing computational resources on a manageable portion of the network that captures the essential dynamics, while avoiding the excessive computational burden of full-network optimization.
2Quantity of substance
If revenue-generating content is increased, then revenue is improved, but user engagement decreases
Solution Approach 1:
The patent employs multi-objective optimization to dynamically adjust the parameters of content display, including the quantity and type of revenue-generating content shown to users. The optimization algorithm simultaneously considers revenue generation and user engagement metrics, finding the optimal balance point where revenue is maximized without causing unacceptable degradation in user engagement. This allows the system to adapt content strategies based on real-time performance data and user responses.
Solution Approach 2:
The patent applies different content strategies and optimization parameters to different user subsets based on their specific characteristics, preferences, and engagement patterns. Rather than applying a uniform revenue-maximization strategy across all users, the system tailors the mix of revenue-generating and non-revenue content to each subset's preferences, thereby maintaining high user engagement while still achieving revenue goals through localized optimization.
3Productivity
If a subset of users is used for optimization, then computational efficiency is improved, but statistical representativeness may be reduced
Solution Approach 1:
The patent segments the user base into multiple subsets based on diverse characteristics including demographics, behavior patterns, and engagement levels. By creating multiple segmented subsets rather than using a single random sample, the system ensures that each subset is statistically representative of specific user segments while collectively covering the entire user population. This segmentation approach maintains computational efficiency while preserving statistical validity.
Solution Approach 2:
The patent designs the subset selection methodology to serve multiple functions simultaneously: it reduces computational burden by limiting the optimization scope, ensures statistical representativeness through careful sampling strategies, and enables the results to be generalized across the entire user base. The subsets are constructed to be representative of broader user populations, allowing findings from subset optimization to be universally applied to improve the entire social network.
Data Source
AI summary
This disclosure relates to systems and methods that include a member activity database including data indicative of interactions with content items on a social network by a population of users of the social network. A processor is configured to obtain an optimization criterion based on at least two constraints related to a performance of the social network, obtain, for a subset of the population of users, at least some of the data indicative of interactions with content items from the member activity database, determine, based on the at least some of the data as obtained, an operating condition for the social network that is estimated to meet the optimization criterion, and provide, to at least some of the user devices via the network interface, the social network based, at least in part, on the operating condition.