Neural Network Dimensionality Reduction for Cross-Category Recommendations
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Solution Overview
Problem
The sheer volume of data for products in online retailers makes it impractical to utilize all available data for generating accurate recommendations, leading to less accurate results in content and advertisement generation.
Innovation Solution
A neural network-based approach that reduces data dimensionality by rolling categories up to higher-level nodes, using user signatures generated from hidden layers to train category-specific models for generating recommendations and advertisements across categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all available product data is utilized for generating recommendations, then recommendation accuracy would improve, but processing complexity and resource requirements become impractical
Solution Approach 1:
The patent segments the product catalog into multiple categories and trains separate neural network models for each category. Each category-specific model processes only the subset of data relevant to that category, rather than processing all product data. This segmentation reduces the computational complexity and resource requirements while maintaining recommendation accuracy for each specific category.
2Measurement precision
If all available product data is utilized for generating recommendations, then recommendation accuracy would improve, but processing time and resource consumption increase
Solution Approach 1:
The patent divides the large product dataset into smaller category-specific datasets and processes them in parallel using multiple neural network models. This segmentation enables more efficient processing by distributing the computational load across multiple models, each handling a manageable subset of data, thereby improving overall processing efficiency while maintaining comprehensive coverage.
Solution Approach 2:
The patent performs preliminary action by pre-training category-specific neural network models on category-specific data before generating recommendations. This pre-processing step creates optimized models that can quickly generate accurate recommendations without requiring real-time processing of all product data, thus improving processing efficiency.
Data Source
AI summary
The dimensionality of user data can be reduced in order to allow cross-category data to be used to determine recommendations, advertising, or other supplemental content within a specific category. A first reduction in dimensionality results from rolling up category nodes to higher-level nodes. User data for the higher-level nodes can be used to train a neural network, with a user signature being generated using node values from a hidden layer of the trained model. The user signature can then be used to train a category specific model in order to obtain category-specific recommendations, determine category-specific advertising, or select other supplemental content based at least in part upon cross-category data.