LSTM Autoencoder for Dynamic User Trait Embeddings
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Solution Overview
Problem
Conventional digital analytics engines struggle to efficiently process and analyze heterogeneous user trait data that changes over time, leading to inaccurate user representation and comparison, as they rely on static user traits and lack the ability to encode dynamic trait changes effectively.
Innovation Solution
A system that generates vectorized user embeddings using a neural network, specifically a long short-term memory (LSTM) autoencoder model, to transform user trait data into uniform representations, capturing the journey of user traits over time and enabling efficient comparison and segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional analytics engines use static user trait information, then the system complexity is reduced, but the user representation accuracy deteriorates
Solution Approach 1:
The patent transforms static user trait representations into dynamic sequences that capture temporal evolution of user traits. By modeling user traits as time-varying sequences and applying sequential processing techniques, the system adapts to changing user characteristics while maintaining manageable complexity through structured temporal modeling.
Solution Approach 2:
The patent changes the fundamental parameter representation from static snapshots to temporal sequences. By transforming user trait data into sequential formats with temporal dependencies, the system enables more accurate representation of user evolution while using standardized sequence processing methods to control computational complexity.
2Adaptability or versatility
If conventional analytics engines process heterogeneous user trait data, then the user behavior analysis capability is improved, but the processing efficiency deteriorates
Solution Approach 1:
The patent transforms heterogeneous user trait data into uniform sequential representations. By converting diverse user behavior data into standardized temporal sequences with consistent formatting, the system enables efficient processing while preserving the ability to analyze different user behavior patterns through the unified sequence framework.
Solution Approach 2:
The patent introduces sequential representations as an intermediary layer between raw heterogeneous user trait data and analysis algorithms. This intermediate sequence format serves as a universal representation that bridges diverse data sources and analysis methods, improving both versatility and efficiency.
3Loss of energy
If conventional analytics engines treat users with same current traits as identical, then the computational resources are reduced, but the user comparison accuracy deteriorates
Solution Approach 1:
The patent performs preliminary encoding of user trait sequences into compact representations before comparison operations. By pre-processing user sequences into condensed formats that capture essential temporal patterns, the system reduces computational resources needed for comparison while maintaining or improving accuracy through the informative compressed representations.
Solution Approach 2:
The patent creates compressed copies of user trait sequences that preserve essential temporal characteristics. These condensed representations serve as efficient proxies for full sequence comparisons, reducing computational overhead while maintaining the ability to distinguish between different user trajectories.
Data Source
AI summary
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an LSTM autoencoder model that captures a history of changes to user trait data. For example, the user embeddings system identifies user trait changes from the user trait data over time as well as generates user trait sequences. Further, the user embeddings system can utilize the user trait sequences to train an LSTM neural network in a semi-supervised manner and generate uniform user embeddings for users.