Sequence-Invariant User Intention Prediction for Real-Time Engagement
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Solution Overview
Problem
Existing machine learning systems face challenges in accurately predicting user intentions in real-time due to latency issues between event detection and outcome determination, which affects the quality and accuracy of user engagement strategies.
Innovation Solution
A network computing system that employs a sequence invariant model to determine user intentions by monitoring real-time activities and utilizing predictive models adapted for sequences of activities, thereby reducing latency and improving accuracy in user engagement strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning systems analyze and act on stored data in batch during off-hours, then processing accuracy can be maintained, but real-time user engagement capability deteriorates due to latency
Solution Approach 1:
The system dynamically adapts its processing mode based on real-time requirements. It uses streaming data processing for immediate user engagement decisions while maintaining batch processing capabilities for comprehensive analysis, allowing the system to switch between real-time and batch modes as needed to balance latency and accuracy requirements
Solution Approach 2:
The machine learning system is segmented into multiple components: real-time streaming processors that handle immediate predictions with acceptable latency, and batch processors that perform comprehensive analysis for model retraining. This segmentation allows different parts of the system to operate at different speeds and accuracy levels appropriate to their specific functions
2Measurement precision
If the system monitors all user activities in real-time to improve engagement accuracy, then user experience improves, but system complexity and computational resources increase
Solution Approach 1:
The system applies different levels of monitoring and analysis to different user activities based on their importance. High-value activities such as conversion events receive intensive real-time analysis, while routine activities use lighter processing. This local quality approach maintains prediction accuracy for critical events while reducing overall system complexity
Solution Approach 2:
The system performs partial monitoring of all activities but focuses intensive analysis only on relevant events. It uses sampling and selective deep analysis rather than exhaustive processing of every user action, achieving good prediction accuracy without the full computational overhead of complete real-time monitoring
Data Source
AI summary
A network computer system and method are provided in which each user of a group of users is monitored during a respective online session where the user performs a sequence of M activities, to selectively engage users of the group. A determination is made as to the impact of friction for each user of the group of users with respect to an intention of the respective user, and an action is performed for the user based at least in part on the determined impact of friction.