Predictive Content Generation for User Interest Adaptation
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Solution Overview
Problem
Content providers face challenges in predicting and adapting to changes in user interests, particularly due to unexpected or irregular events, leading to manual or complex adjustments in content generation, which can be reactive rather than predictive.
Innovation Solution
A system that generates predictions of user interest in events by analyzing time series data, expected event data, and irregular event data, using machine learning models to forecast interest levels before, during, and after events, and uses these predictions to display relevant content to users at optimal times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual modification of content is used to react to user interest changes, then content can be adjusted to match user interests, but the response time is delayed and operational efficiency decreases
Solution Approach 1:
The system performs preliminary actions by training machine learning models on historical data in advance, enabling these models to automatically predict user interest changes and trigger content modifications proactively before users actually change their interests, eliminating the need for manual intervention and reducing response time
Solution Approach 2:
The system implements continuous feedback loops where user interaction data is collected, analyzed by machine learning models to detect interest changes, and used to automatically adjust content recommendations, creating a closed-loop system that continuously adapts to user preferences without manual intervention
2Adaptability or versatility
If complex engineering of generic experimentation setup is used to generate content following user interest changes, then content can adapt to user interests, but device complexity and implementation difficulty increase
Solution Approach 1:
The system enables self-service by implementing automated machine learning models that independently analyze user data, predict interest changes, and trigger content modifications without requiring manual configuration or complex experimentation setups, simplifying the overall system architecture
Solution Approach 2:
The patent applies multi-functionality by using a single machine learning framework that handles multiple tasks including user behavior analysis, interest prediction, and content recommendation optimization, replacing the need for separate complex experimentation systems
3Adaptability or versatility
If reactive or post-hoc content modification is used, then content can eventually match user interests, but productivity and operational efficiency decrease
Solution Approach 1:
The system performs preliminary actions by continuously training machine learning models on historical data in advance, enabling these models to automatically predict user interest changes and trigger content modifications proactively before users actually change their interests, eliminating the need for manual intervention and reducing response time
Solution Approach 2:
The system implements continuous feedback loops where user interaction data is collected, analyzed by machine learning models to detect interest changes, and used to automatically adjust content recommendations, creating a closed-loop system that continuously adapts to user preferences without manual intervention
Data Source
AI summary
Systems, devices, and techniques are disclosed for content generation for user interests. Topic phrases may be received. Image content and text content may be generated based on the topic phrases using generative systems. A first generative system may generate the image content and a second generative system may generate the text content. Additional topic phrases may be generated with an auto-summarization system based on the image content. Additional image content and additional text content may be generated based on the additional topic phrases. A third generative system may generate items of candidate content based on the image content, additional image content, text content, and additional text content. The items of candidate content may include an image from either the image content or the additional image content and text from the text content or the additional text content.