User Sensitivity Score for Search Result Content Selection
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Solution Overview
Problem
Existing online systems cannot differentiate between users based on their sensitivity to content relevance, leading to the same sponsored content being suggested to users with varying tolerance levels for relevance.
Innovation Solution
The system employs machine learning models to determine a user's sensitivity score, dynamically selecting content items based on this score and their relevance to the search query, thereby tailoring content presentation to individual user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the system suggests sponsored content items to all users regardless of their sensitivity, then the system can maintain a simple content selection process, but users with low tolerance for irrelevant content become irritated and engagement decreases
Solution Approach 1:
The patent segments users into different sensitivity groups based on their tolerance for irrelevant content. By dividing the user base into segments with different characteristics, the system can apply different content selection strategies to each segment, thereby maintaining high engagement across diverse user preferences without overwhelming complexity
Solution Approach 2:
The system dynamically adjusts content selection based on real-time sensitivity scores and engagement metrics. The content recommendation process is made adaptive rather than static, allowing the system to respond to individual user preferences and change over time, resolving the contradiction between simplicity and effectiveness
2Adaptability or versatility
If the system applies complex machine learning models to determine user sensitivity scores, then content can be tailored to individual user preferences, but system complexity increases
Solution Approach 1:
The patent applies partial personalization by focusing machine learning resources on the most influential factors affecting user engagement. Rather than attempting to model every aspect of user behavior, the system identifies and targets key sensitivity indicators, achieving effective personalization with reduced model complexity
Solution Approach 2:
The system introduces sensitivity scores as intermediary metrics that bridge raw user behavior data and content selection decisions. These intermediate representations simplify the complexity of direct user modeling while preserving the ability to deliver personalized content, acting as a mediator between data and action
3Reliability
If the system presents highly relevant content to all users, then user satisfaction increases, but exploration opportunities are limited for users who are open to discovery
Solution Approach 1:
The patent applies different content relevance qualities to different user segments based on their sensitivity characteristics. Users with low sensitivity to relevance receive more diverse, exploratory content, while users with high sensitivity receive highly relevant content. This local differentiation resolves the contradiction by allowing both satisfaction and exploration to flourish in appropriate contexts
Data Source
AI summary
Content items are presented to users based on sensitivity scores indicating sensitivity levels of users to relevance of content items to queries. A system receives a query from a target user, retrieves a set of search results responsive to the query, and retrieves a set of content items, each of which has a relevance score to the query. The system applies a machine learning model to user data of the target user to output a sensitivity score, indicating a sensitivity level of the target user to relevance of content item to the query. The system then selects one or more content items based on the sensitivity score and the relevance scores of the content items, incorporates the selected content items into the search results, and sends the search results with the selected content items for display to the target user.