Hidden Markov Models for Low-Data Account Interaction Prediction
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Solution Overview
Problem
Conventional systems for predicting account interactions with computing applications are inflexible, inaccurate, and inefficient, often requiring large datasets of historical data and imposing unrealistic assumptions, leading to unreliable and outdated predictions.
Innovation Solution
Utilizing customized hidden Markov models generated with neural networks to predict account interactions, including initial state, transition, and emission probability matrices, allowing for more accurate and flexible predictions of user account events such as subscription activations, deactivations, and retentions, and calculating customer lifetime value (LTV) based on individual user data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional prediction systems are used, then they can provide basic predictions, but they require large datasets of historical data and impose unrealistic assumptions, leading to inaccurate and outdated predictions
Solution Approach 1:
The patent changes the fundamental parameters of the prediction model by using Hidden Markov Models with customized transition and emission probability matrices generated by neural networks. This allows the system to make accurate predictions without requiring large historical datasets, as the model learns from limited data and generalizes effectively.
Solution Approach 2:
The patent replaces conventional statistical prediction methods with a neural network-based Hidden Markov Model system. This substitution enables the system to achieve high prediction accuracy with minimal historical data by using deep learning techniques to capture complex patterns in user behavior.
2Reliability
If conventional prediction systems are used, then they can make predictions, but they are inflexible and impose unrealistic assumptions, leading to unreliable predictions
Solution Approach 1:
The patent implements dynamic prediction capabilities through the Hidden Markov Model, where transition probability matrices capture the evolution of user states over time. This dynamic approach allows the model to adapt to changing user behaviors and provide reliable predictions that reflect real-world complexity without imposing unrealistic assumptions.
Solution Approach 2:
The patent applies local quality by generating customized transition and emission probability matrices specific to each user account based on their individual behavior patterns. This allows the model to make reliable predictions for each user while maintaining flexibility across the entire user base, avoiding the need for rigid universal assumptions.
3Productivity
If conventional prediction systems are used, then they can provide predictions, but they are inefficient and require large computational resources
Solution Approach 1:
The patent segments the prediction problem by generating individualized Hidden Markov Models for each user account, where each model is trained on that user's specific historical data. This segmentation allows the system to make efficient predictions for each user independently, reducing overall computational resource requirements compared to training a single large model on all user data.
Solution Approach 2:
The patent uses copying by creating simplified representations of user behavior through customized probability matrices that capture essential patterns. These matrices serve as compact models that can be efficiently processed to generate predictions, reducing computational complexity while maintaining prediction quality.
4Measurement precision
If conventional prediction systems are used, then they can make predictions, but they rely on outdated data and cannot capture subscriber behavior dynamics
Solution Approach 1:
The patent implements feedback mechanisms where the Hidden Markov Model continuously updates its transition and emission probability matrices based on new user behavior data. This feedback loop ensures the model captures the latest subscriber behavior dynamics and provides accurate predictions that reflect current trends rather than relying on outdated historical data.
Data Source
AI summary
Embodiments include a method, apparatus, system and computer-readable medium for generating a set of input features based on user account data associated with a user account, generating a hidden Markov model based on the set of input features, generating a predicted subscription probability matrix comprising probability values representing potential account interactions between the user account a set of computing applications, modifying one or more probability values of the predicted subscription probability matrix to form a modified predicted subscription probability matrix, and determining a predicted account interaction metric for the user account based on the modified predicted subscription probability matrix. Other embodiments are described and claimed.