Dynamic Sponsored Content Policy Relaxation for User Tolerance
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Solution Overview
Problem
Conventional methods for regulating the presentation of sponsored content in online systems often reduce user interaction by adhering to strict policies, which can lead to user frustration and decreased engagement, especially when these policies prevent sponsored content from being placed in positions that could increase user interaction.
Innovation Solution
An online system relaxes content policies for sponsored content items based on specific criteria, such as bid amount, user interaction likelihood, and tolerance models, allowing sponsored content to be placed in positions that violate traditional policies if the benefits outweigh the penalties, thereby increasing user interaction and revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If strict content policies are applied to regulate sponsored content presentation, then user frustration is reduced and organic content visibility is maintained, but user interaction and revenue are decreased
Solution Approach 1:
The system dynamically adjusts content policies based on user characteristics and context. Instead of applying fixed strict rules, the policy enforcement adapts to individual user tolerance levels, allowing sponsored content placement to vary per user while maintaining overall user satisfaction and interaction rates
Solution Approach 2:
The system changes the parameter of policy strictness based on user characteristics. By modeling user tolerance for sponsored content and using this information to adjust policy application, the system optimizes the balance between maintaining organic content visibility and maximizing user interaction and revenue
2Productivity
If sponsored content is placed in high-visibility positions to increase revenue, then revenue is increased, but user experience deteriorates and user interaction decreases
Solution Approach 1:
The system applies different quality standards for sponsored content placement based on user characteristics. Instead of uniform placement rules, the system tailors sponsored content visibility and positioning to individual user tolerance levels, allowing high-revenue placements for users with high tolerance while protecting users with low tolerance from excessive sponsored content
Solution Approach 2:
The system changes the placement parameters of sponsored content based on user tolerance models. By adjusting visibility and positioning parameters according to individual user characteristics, the system maximizes revenue from users who tolerate sponsored content while maintaining good user experience for those who are more sensitive
3Reliability
If conventional policy rules are applied uniformly to all users, then policy compliance is maintained, but user interaction likelihood is reduced
Solution Approach 1:
The system transitions from static uniform policy application to dynamic personalized policy enforcement. By continuously modeling user tolerance and adjusting policy application accordingly, the system maintains compliance while adapting to individual user preferences and behaviors
Solution Approach 2:
The system segments users based on their tolerance characteristics for sponsored content. By dividing the user base into segments with different tolerance levels and applying customized policy rules to each segment, the system achieves both policy compliance and optimized user interaction
Data Source
AI summary
An online system applies content policies regulating presentation of sponsored content to its users. For example, content policies may prevent the presentation of sponsored content items in certain positions content feeds. The online system may relax a content policy when generating a content feed for a user based on characteristics of a user. For example, the online system generates a model determining a tolerance of the user for sponsored content, and relaxes one or more content policies if the tolerance of the user for sponsored content equals or exceeds a threshold. As another example, the online system determines whether to relax one or more content policies based on a comparison of a historical amount of compensation received from the user and an expected amount of compensation from presenting content items violating a content policy.