Machine Learning Architecture for Content Interaction Prediction
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Solution Overview
Problem
Existing systems are inefficient in predicting user interactions with content items, leading to wastage of network and computing resources, and result in latency due to ineffective computational techniques for identifying relevant content.
Innovation Solution
The implementation of machine learning architectures with a feature interaction layer and multi-level extraction layers, including computational experts models and gating networks, to analyze user profiles and determine probabilities of content interactions, optimizing resource use and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing computational techniques are used to identify relevant content, then the system can process user interactions, but network and computing resources are wasted and latency increases
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on historical user interaction data before actual content delivery. This pre-computation enables the system to predict user actions in advance, so that when content is actually presented to users, the predictions are already available, avoiding real-time computational waste and reducing latency.
Solution Approach 2:
The patent replaces traditional mechanical filtering and rule-based content selection systems with machine learning-based predictive models. These models use pattern recognition and probabilistic reasoning to predict user actions, substituting inefficient computational mechanics with more intelligent, data-driven approaches that reduce resource consumption while improving accuracy.
2Productivity
If existing computational techniques are used to identify relevant content, then the system can deliver content to users, but latency increases due to ineffective processing
Solution Approach 1:
The system performs preliminary analysis by training machine learning models on historical user interaction data before actual content delivery. This pre-computation enables the system to predict user actions in advance, so that when content is actually presented to users, the predictions are already available, avoiding real-time computational waste and reducing latency.
Solution Approach 2:
The patent segments the content delivery system into distinct components: historical data collection, model training phase, and real-time prediction phase. By separating these functions, the system can perform intensive computational work during off-peak training periods and only execute lightweight predictions during user interactions, thereby reducing operational latency.
3Adaptability or versatility
If traditional content delivery is used, then all content can be presented to users, but network and computing resources are allocated inefficiently
Solution Approach 1:
The patent applies local quality by tailoring content delivery to individual user characteristics and preferences. Instead of uniform content presentation, the system analyzes each user's historical interactions and predicts their specific actions on different content items. This localized, personalized approach ensures that computational and network resources are allocated only to content likely to engage specific users, improving overall resource efficiency.
Solution Approach 2:
The system dynamically changes delivery parameters such as content selection, formatting, and timing based on predicted user actions. By adjusting these parameters according to individual user profiles and predicted responses, the system optimizes resource allocation while maintaining high adaptability to diverse user preferences and behaviors.
Data Source
AI summary
Machine learning architectures may predict the likelihood of interaction by users with content items that are accessible using a client application. The machine learning architectures may include one or more feature interaction layers that are coupled with one or more extraction layers. Content items may be selected to provide to users of the client application based on probabilities of users performing one or more actions with respect to the content items, where the probabilities for each action are determined by the machine learning architectures.