Neural Network Embedding Dimension Selection for Recommender Models
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Solution Overview
Problem
Existing recommender systems inefficiently allocate the same embedding dimension to all feature fields, leading to resource waste and poor performance due to overfitting, as they fail to account for the varying informational value and cardinality of different feature fields.
Innovation Solution
A framework that automatically selects optimal embedding dimensions for different feature fields using an end-to-end differentiable approach, iteratively optimizing neural network parameters and weights, and retraining the recommender system with the determined embedding architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the same embedding dimension is allocated to all feature fields, then the system is simple to implement, but it leads to resource waste and poor performance due to overfitting
Solution Approach 1:
The patent applies local quality by assigning different embedding dimensions to different feature fields based on their specific characteristics. Each feature field receives a customized embedding dimension that matches its informational value and cardinality, rather than using a uniform dimension for all features. This resolves the contradiction by maintaining simple implementation through automated algorithms while achieving high performance through optimized, field-specific embedding dimensions.
Solution Approach 2:
The patent changes the parameter of embedding dimension from a fixed uniform value to variable values specific to each feature field. The system automatically determines optimal embedding dimensions for different feature fields based on their characteristics, transforming the single-parameter approach into a multi-parameter approach that adapts to each feature's requirements, thereby improving performance without significantly complicating implementation.
2Reliability
If larger embedding dimensions are used for all features, then feature representation capability is improved, but memory consumption and computing resources increase
Solution Approach 1:
The patent applies local quality by matching embedding dimension size to the specific needs of each feature field. High-cardinality features receive larger embedding dimensions to capture their complexity, while low-cardinality features receive smaller dimensions. This prevents uniform over-allocation of memory resources while ensuring adequate representation capability for each feature type, resolving the contradiction between representation quality and memory efficiency.
Solution Approach 2:
The patent uses partial action by allocating embedding dimensions only as large as necessary for each feature field's requirements. Instead of applying excessive embedding dimensions uniformly to all features, the system provides just enough dimensionality for each feature's informational needs, reducing overall memory consumption while maintaining sufficient representation capability where required.
3Quantity of substance
If smaller embedding dimensions are used for all features, then memory efficiency is improved, but information loss occurs in high-cardinality feature fields
Solution Approach 1:
The patent applies local quality by differentiating embedding dimension allocation based on feature field characteristics. High-cardinality features receive larger embedding dimensions to preserve their information content, while low-cardinality features use smaller dimensions for memory efficiency. This resolves the contradiction by ensuring information preservation only where necessary, rather than uniformly across all features.
Solution Approach 2:
The patent changes the embedding dimension parameter dynamically based on feature field cardinality and informational value. The system automatically adjusts the dimension parameter for each feature field, allocating larger dimensions to high-cardinality features that would suffer information loss with small dimensions, while using small dimensions for low-cardinality features where memory efficiency is prioritized, thus resolving the information loss contradiction.
4Reliability
If automated embedding dimension selection is implemented, then performance is improved, but system complexity increases
Solution Approach 1:
The patent applies self-service by implementing automated algorithms that independently determine optimal embedding dimensions for each feature field without requiring manual configuration. The system self-adjusts the embedding architecture based on feature characteristics and performance metrics, resolving the contradiction by automating the complex optimization process while maintaining implementation simplicity through black-box automated decision-making.
Solution Approach 2:
The patent applies preliminary action by pre-determining optimal embedding dimensions through automated analysis of feature field characteristics before model training. The system performs preliminary optimization to establish the embedding architecture, which then remains fixed during training, resolving the contradiction by front-loading the complexity into an automated preliminary configuration step rather than requiring complex ongoing management.
Data Source
AI summary
Methods, systems, and computer programs are presented for determining parameters of neural networks and selecting embedding dimensions for the feature fields. One method includes an operation for initializing parameters of a neural network and weights for embedding sizes for each feature associated with the neural network. The parameters of the neural network and the weights are iteratively optimized. Each optimization iteration comprises training the neural network with current parameters of the neural network to optimize a value of the weights, and training the neural network with current values of the weights to optimize the parameters of the neural network. Further, the method includes operations for selecting embedding sizes for the features based on the optimized values of the weights, and for training the neural network based on the selected embedding sizes for the features to obtain an estimator model. A prediction is generated utilizing the estimator model.