Content Selection Using Predicted User Interaction Metrics
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Solution Overview
Problem
Users often have to actively seek online content relevant to their interests, and existing methods lack efficiency in predicting the likelihood of user interactions with content, leading to suboptimal content selection and presentation.
Innovation Solution
A method and system that utilize stored user interest data to select third-party content by analyzing historical actions and predicting action metrics, such as click-through rates, to determine the likelihood of user interactions, thereby optimizing content selection for presentation alongside first-party content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content selection is based on user interest data and prediction models, then user interaction rates improve, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by collecting user interaction data and training prediction models in advance. Interest categories and user profiles are pre-computed based on historical behavior, enabling fast content selection without real-time complex analysis. This resolves the contradiction by shifting computational complexity from the selection moment to preliminary offline processing.
Solution Approach 2:
The patent introduces intermediate structures including interest category classifications, user profile summaries, and prediction model layers that mediate between raw user data and content selection decisions. These intermediaries simplify the decision-making process by transforming complex user behavior patterns into structured interest categories that can be efficiently matched with content, thereby improving interaction rates without proportionally increasing system complexity.
2Measurement precision
If comprehensive user data is collected and analyzed, then content selection accuracy improves, but data processing time increases
Solution Approach 1:
The system extracts and isolates only the most relevant features from comprehensive user data for content selection. Instead of analyzing all available user information in real-time, the patent extracts pre-computed interest category indicators and key behavioral metrics that are stored in user profiles. This extraction approach maintains high selection accuracy by focusing on the most predictive features while dramatically reducing the data processing time required during content selection.
Solution Approach 2:
Comprehensive user data analysis is performed in advance to build detailed user profiles containing interest categories and behavioral patterns. This preliminary action allows the system to store processed insights rather than raw data, enabling accurate content selection without requiring real-time processing of comprehensive user information. The trade-off is resolved by performing heavy data processing offline before the selection moment.
Data Source
AI summary
Systems and methods for predicting content performance with interest data include receiving a content selection request that includes a client identifier. One or more topical interest categories associated with the client identifier may be used as inputs to a prediction model to predict the likelihood of an online action occurring as a result of third-party content being selected. The predicted likelihood may be used to select third-party content.