Visual Search Feature Vector Manipulation for Result Diversity
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Solution Overview
Problem
Existing electronic search systems in online marketplaces struggle to efficiently find visually similar items due to limitations in keyword-based approaches, which fail to accurately capture and filter items based on visual attributes like color, shape, and style, especially for items that don't fit strict categories.
Innovation Solution
The implementation of visual search techniques using neural networks to extract feature vectors from image data, allowing for the manipulation of these vectors to balance similarity and diversity in search results, enabling users to find both accurate and diverse recommendations based on visual attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword-based search approaches are used, then the search system is simple to implement, but the accuracy of visual attribute filtering is poor
Solution Approach 1:
The patent replaces keyword-based text processing with neural network-based image analysis. The system uses convolutional neural networks to automatically extract visual features (color, shape, texture, style) from product images, substituting manual keyword tagging with automated visual recognition. This resolves the contradiction by achieving high-precision visual attribute filtering through machine learning while maintaining system simplicity through automated feature extraction.
Solution Approach 2:
The system transforms the search approach by changing from text-based parameter matching to visual feature vector matching. Product images are converted into high-dimensional feature vectors containing multiple visual attributes, enabling precise filtering based on visual similarity rather than keyword overlap. This parameter transformation allows accurate visual attribute filtering without requiring complex manual categorization systems.
2Adaptability or versatility
If strict category-based filtering is applied, then search results are precise, but diverse items are excluded
Solution Approach 1:
The patent implements dynamic search results by allowing users to adjust the similarity threshold parameter. The system calculates cosine similarity between query feature vectors and product feature vectors, then dynamically filters results based on user-selected threshold values. This enables the search system to adapt between precision (high threshold) and diversity (low threshold) based on user needs, resolving the contradiction between accurate matching and result diversity.
Solution Approach 2:
The system moves from traditional single-dimension category filtering to multi-dimensional visual feature space. By representing products in high-dimensional feature vectors encompassing color, shape, texture, and style dimensions, the system can perform similarity searches across multiple attributes simultaneously. This dimensional expansion allows precise visual matching while naturally incorporating diversity through partial matches on different feature dimensions.
3Measurement precision
If visual search using neural networks is implemented, then search accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing feature vectors for all products in the database during indexing. When a search query is executed, the system only needs to compute the feature vector for the query image and compare it against pre-computed product vectors using efficient cosine similarity calculation. This preprocessing approach significantly reduces computational complexity during actual search operations while maintaining high visual search accuracy.
Solution Approach 2:
The system creates compressed representations (feature vectors) of product images that capture essential visual characteristics in a compact numerical form. Instead of processing full-resolution images during search, the system works with these copied feature representations, dramatically reducing computational requirements while preserving visual attribute information needed for accurate matching.
4Adaptability or versatility
If feature vectors are manipulated to increase diversity, then result variety is improved, but similarity precision decreases
Solution Approach 1:
The patent implements feedback mechanisms where user interactions with search results (clicks, purchases, skips) are used to refine future search recommendations. The system monitors which diverse results users engage with and adjusts the similarity threshold and feature weighting accordingly. This feedback loop allows the system to maintain precision by learning from user preferences while systematically introducing diversity through controlled parameter adjustments, resolving the contradiction between similarity accuracy and result variety.
Data Source
AI summary
The present embodiments provide visual search techniques which produces results that include both accurate similar items as well diversified items through attribute manipulation. In some embodiments, a feature vector describing the item of interest is obtained. A target feature vector is then generated at least partially from the original feature vector, in which the target feature vector shares only a subset of attribute values with the original feature vector and includes at least some values that are different from the original feature vector. An electronic catalog of items is then queried using the target feature vector, and a set of candidate items are determined from the electronic catalog based at least in part on similarity to the target feature vector. The original feature vector may be used to query for a set of similar items that are as similar as possible to the item of interest.