Neural Network Embedding Vectors for Visual Item Features
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Solution Overview
Problem
Current recommendation systems in online platforms, such as collaborative filtering and content-based systems, are limited in accurately identifying and recommending items based on visual features from images, as they do not effectively leverage machine learning to generate precise embedding vectors for item features like identity, category, shape, and color.
Innovation Solution
A neural network is trained on multiple images of an item to generate embedding vectors for various features, iteratively refining these vectors by comparing probability vectors with actual value vectors to reduce error, allowing for accurate representation and recognition of item features across diverse images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional recommendation systems (collaborative filtering or content-based) are used, then item recommendations can be provided, but the systems cannot accurately identify and recommend items based on visual features from images
Solution Approach 1:
The patent replaces traditional recommendation system approaches (collaborative filtering and content-based systems) with a neural network-based machine learning system. The neural network processes images directly to generate embedding vectors for item features, substituting the mechanical rule-based or statistical approaches of traditional systems with a learning-based visual feature extraction system that achieves both accuracy and visual adaptability
Solution Approach 2:
The patent transforms visual features from images into numerical embedding vectors through a neural network. This parameter transformation converts raw pixel data into meaningful feature representations (identity, category, shape, color) that can be accurately compared and used for recommendation, enabling the system to work with visual data in a mathematically tractable form
2Measurement precision
If a neural network is trained on multiple images to generate embedding vectors, then accurate representation of item features is achieved, but the training process requires iterative refinement and significant computational resources
Solution Approach 1:
The patent performs preliminary training of the neural network on a large dataset of images with known item features before deployment. This preliminary action pre-learns the mapping from visual features to embedding vectors, so that when the system is deployed, it can quickly generate accurate embeddings without requiring real-time iterative refinement, thus reducing operational time loss
Solution Approach 2:
The patent implements an iterative training process where the neural network generates embedding vectors, these vectors are compared with actual value vectors to compute error, and the network parameters are adjusted to reduce this error. This feedback loop continues until convergence, ensuring high accuracy of the embedding vectors while systematically reducing training time through guided optimization
Data Source
AI summary
Machine learning multiple features of an item depicted in images. Upon accessing multiple images that depict the item, a neural network is used to machine train on the plurality of images to generate embedding vectors for each of multiple features of the item. For each of multiple features of the item depicted in the images, in each iteration of the machine learning, the embedding vector is converted into a probability vector that represents probabilities that the feature has respective values. That probability vector is then compared with a value vector representing the actual value of that feature in the depicted item, and an error between the two vectors is determined. That error is used to adjust parameters of the neural network used to generate the embedding vector, allowing for the next iteration in the generation of the embedding vectors. These iterative changes continue thereby training the neural network.