Tensor Train Decompositions for Scalable Segment Modeling
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Solution Overview
Problem
Existing machine learning models struggle to accurately predict the propensity to interact for lower-volume users due to training data being heavily influenced by high-volume users, and multi-task learning does not scale effectively with a large number of segments.
Innovation Solution
The use of tensor train decompositions to create large, personalized layers for efficient segment modeling, allowing for the representation of dense weight matrices in a compact multi-linear format and enabling scalable training and scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-task learning is applied to model different user segments, then prediction accuracy for various segments can be improved, but the system does not scale effectively when the number of segments is large
Solution Approach 1:
The patent applies segmentation by dividing the user base into distinct segments based on activity levels (high-volume, mid-volume, low-volume users). Instead of treating all users uniformly or using complex multi-task learning for every segment, the system creates separate modeling approaches for each segment type. This allows accurate predictions for each segment while avoiding the scalability issues of comprehensive multi-task learning across all possible segment combinations.
2Reliability
If training data is used from real-world user interactions, then the model can learn from actual behavior patterns, but the data becomes heavy with information from high-volume users making it difficult to accurately predict for lower-volume users
Solution Approach 1:
The patent applies local quality by treating different user segments with different modeling approaches tailored to their specific characteristics. High-volume users receive one type of modeling treatment while low-volume users receive another. This allows the system to optimize predictions for each segment's specific needs rather than applying a uniform approach, thereby improving accuracy for lower-volume users who were previously overwhelmed by high-volume user data patterns.
Solution Approach 2:
The patent applies partial action by selectively applying different modeling strategies to different segments rather than using a comprehensive approach for all users. The system performs multi-task learning or specialized modeling only where needed for specific segments, rather than attempting to model all user interactions uniformly. This partial application of specialized techniques improves predictions for underserved segments while maintaining overall system efficiency.
Data Source
AI summary
In an example embodiment, tensor train decompositions are used to create large, personalized layers that are efficient for segment modeling. More particularly, rather than performing learning on an input matrix of training data that contains all segments, and then crossing this matrix with a vector for a particular segment, the matrix is mapped to an N-dimensional tensor, where each of the dimensions corresponds to one of the properties used to compose the segment, which can then be approximated by tensor train decomposition to enable efficient training and scoring.