Neural Collaborative Filtering for Subscription At-Risk Prediction
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Solution Overview
Problem
Existing collaborative filtering methods, such as matrix factorization, face challenges in accurately predicting user-item interactions in low-dimensional latent spaces, leading to ranking losses and overfitting, especially in sparse data settings.
Innovation Solution
A neural collaborative filtering model is employed to predict account health and subscription changes by transforming raw account data into subsets of modified and unmodified subscriptions, determining an interaction function using a machine learning model, and identifying at-risk items based on their modification probabilities, without requiring content data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If matrix factorization with fixed inner product function is used, then model simplicity is maintained, but prediction accuracy deteriorates due to inability to capture complex user-item interactions
Solution Approach 1:
The patent transforms the fixed inner product function into a learnable parameterized function. The interaction function f(u, i; Θ) uses parameters Θ that are optimized during training, allowing the model to adapt to complex user-item interactions while maintaining a relatively simple architectural structure. This resolves the contradiction by making the function flexible through parameter learning rather than structural complexity.
Solution Approach 2:
The patent introduces dynamic adaptability by replacing the static inner product with a dynamic interaction function that can be adjusted during training. The function adapts to different user-item pairs by learning optimal parameters, enabling the model to capture complex interaction patterns without requiring a complex overall model structure.
2Measurement precision
If the number of latent factors K is increased, then ranking loss is reduced, but model generalization deteriorates due to overfitting in sparse settings
Solution Approach 1:
Instead of increasing the dimensionality K to improve ranking accuracy, the patent changes the interaction function parameters Θ to capture complex patterns. This approach achieves better ranking accuracy without increasing model capacity excessively, thereby avoiding overfitting in sparse data settings and maintaining good generalization.
Solution Approach 2:
The patent applies partial action by using a moderate number of latent factors combined with a powerful interaction function. Rather than using excessive latent factors that would cause overfitting, the model uses sufficient factors paired with a learnable interaction function to achieve the necessary ranking accuracy without compromising generalization.
3Productivity
If simple inner product function is used, then computational efficiency is maintained, but interaction modeling capability deteriorates due to inability to capture complex patterns
Solution Approach 1:
The patent replaces the simple inner product with a parameterized interaction function that maintains computational efficiency while improving modeling capability. The function f(u, i; Θ) computes interactions using learned parameters rather than complex multi-layer computations, preserving efficiency while significantly enhancing the ability to capture complex user-item interaction patterns.
Data Source
AI summary
A method, apparatus, system, and computer program code for identifying at-risk items. Raw account data is collected for a set of accounts, each account comprising a set of subscriptions to a set of items. The raw account data is transformed to generate a first subset of account data that comprises only accounts having subscriptions that have been modified, and a second subset of account data that comprises only accounts having subscriptions that are unmodified. An interaction function it is determined, by a machine learning model, according to the first subset of account data. A number of at-risk items is determined, by the machine learning model. Each at-risk item has a respective probability of modification based on the interaction function. The at-risk items are displayed on a graphical user interface.