Taxonomy-Based AI Image Queries for Personalized Item Search
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Solution Overview
Problem
Conventional search engines face limitations in accurately identifying and retrieving relevant information due to the need for a relevant image query in image-based searching and the constraints of text-based queries, especially in scenarios where image capture is not feasible, leading to inefficiencies in navigating the vast amount of online content.
Innovation Solution
A taxonomy-based approach that classifies prior interacted items into categories, generates photo-realistic images using a generative AI model, and uses these images as search queries to enhance the accuracy and personalization of search results, allowing for more comprehensive exploration of databases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image-based searching is used to improve search accuracy, then search result relevance is improved, but the requirement for image capture capability increases system complexity and limits ease of operation
Solution Approach 1:
The system creates synthetic image copies of database items using generative AI models. These generated images serve as search queries instead of requiring users to capture real-world images. The copying principle is applied by generating representative images of items from database records, which can then be used for image-based searching without needing physical image capture devices.
Solution Approach 2:
The system introduces an intermediary component that translates between database records and image representations. This intermediary layer (the image generation model) mediates between the text-based database and image-based search requirements, allowing searches to be performed using generated images while the actual search can be initiated from text-based interfaces, thus bridging the gap between search accuracy and operational convenience.
2Measurement precision
If taxonomy-based classification is applied to improve image generation quality, then search result relevance is improved, but the processing time and system complexity increase
Solution Approach 1:
The system performs preliminary classification of database items into taxonomic categories before image generation. By pre-organizing items into categories and subcategories, the system can quickly select appropriate items for image generation based on search context, avoiding the need to process all items. This preliminary organization reduces the time required for image generation while maintaining high accuracy through category-specific generation parameters.
Solution Approach 2:
The system segments the database into hierarchical taxonomic categories and processes items within each category separately. This segmentation allows parallel processing of different category groups and enables the use of category-specific generation models or parameters, improving overall efficiency while maintaining high image generation quality for each segment.
3Adaptability or versatility
If generative AI models are used to create photo-realistic images from database records, then visual search capability is improved, but computational resources and processing time increase
Solution Approach 1:
The system generates images selectively for only those database items that are most relevant to the search context, rather than generating images for all items. By using taxonomy-based filtering and relevance scoring, the system performs partial action on a subset of items, reducing overall computational load while maintaining high versatility in search capability for the most important items.
Solution Approach 2:
The system dynamically adjusts the level of image generation detail and quality based on search context and user needs. For some queries, highly detailed photo-realistic images are generated, while for others, simpler representations suffice. This dynamic approach optimizes computational resource usage while maintaining adaptability across different search scenarios.
Data Source
AI summary
Taxonomy-based image generation is used for item searching, and enhances the quality and personalization of search results. Prior interacted items are classified into a categorical taxonomy. A generative AI model can be used to classify the prior interacted items, by generating categories or assigning to existing categories. A set of prior interacted items is selected from one of the categories and provided to an image model that generates a photo-realistic image in response. The photo-realistic image includes item renderings that are rendered illustrations of items. An item search using a search engine can be performed based on the generated photo-realistic image. For instance, the photo-realistic image or a portion thereof could be provided as a search query using an image-based search or described to perform a text-based search. Search results are identified for the search query and are provided in response.