Motif Embeddings for Explainable User Classification
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Solution Overview
Problem
Conventional user classification systems using deep learning neural networks lack explainability, making it difficult to understand the rationale behind classification decisions.
Innovation Solution
Utilize motif embeddings as anchors to compare user embeddings with predefined archetypes, providing explainability by identifying the closest motif embedding and quantifying the proximity of user behavior sequences to these archetypes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning neural networks are used for user classification, then classification accuracy is improved, but explainability deteriorates
Solution Approach 1:
The patent introduces motif embeddings as intermediary representations that bridge the gap between raw user behavior data and classification decisions. These motif embeddings serve as interpretable intermediaries that capture meaningful behavior patterns while maintaining the ability to produce accurate classifications, thus resolving the contradiction between accuracy and explainability.
Solution Approach 2:
The patent segments the user behavior sequence into discrete motifs or patterns. By breaking down the complex behavior into segmentable motifs, the system can provide detailed explanations about which specific behavior patterns led to the classification decision, thereby improving explainability while maintaining classification accuracy through the segmented representation.
2Speed
If conventional neural network architectures are used, then processing speed is improved, but decision transparency deteriorates
Solution Approach 1:
The patent creates a simplified copy or representation of the complex neural network processing through motif embeddings. These embeddings capture the essential features extracted by the neural network in a more transparent and interpretable format, allowing for faster processing and better decision transparency without sacrificing the underlying classification performance.
3Measurement precision
If complex behavior sequences are analyzed, then classification granularity is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and isolates the most significant behavior patterns from complex sequences into discrete motifs. By taking out only the essential patterns rather than processing the entire complex sequence, the system achieves high classification granularity for important behaviors while reducing overall computational complexity through selective analysis of extracted motifs.
Data Source
AI summary
Systems and methods for generating explainability for user classifications using motif embeddings. In some aspects, the system generates a first set of embeddings in an embedding space by inputting, to a motif encoder model, a set of event motifs associated with a set of labels. The system generates a second set of embeddings for a first user by providing, to the motif encoder model, an event sequence of the first user. The system selects a set of search anchor embeddings by determining a first set of distances between the second set of embeddings and the first set of embeddings. The system queries the embedding space to select a candidate profile for association with the first user and concurrently presents the explanatory label and a profile title associated with the candidate profile.