AI Query Image Generation for Relevant Search Suggestions
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Solution Overview
Problem
Conventional query auto-completion and suggestion systems in online platforms, such as e-commerce applications, often fail to accurately represent search query suggestions with appealing images, leading to suboptimal user engagement and inefficiencies.
Innovation Solution
An online system employs a machine-learning text-to-image model to generate images for query suggestions, using a ranking model to select the most relevant image for display, and incorporates a large language model for personalized and contextual query auto-completion and suggestion, including supervised fine-tuning and offline caching to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional query auto-completion systems are used, then query suggestions can be provided to users, but the images displayed do not accurately represent the query suggestions and user engagement remains suboptimal
Solution Approach 1:
The patent replaces conventional image selection mechanisms with a machine-learning text-to-image model. Instead of using traditional keyword matching or database lookup to select images, the system uses an AI model that generates images based on text prompts derived from query suggestions. This substitution enables more accurate and appealing visual representations that directly correspond to the semantic meaning of each query suggestion.
2Adaptability or versatility
If multiple stages are used in query suggestion systems, then comprehensive query processing can be achieved, but system complexity increases and success rate in online platforms decreases
Solution Approach 1:
The patent merges multiple separate components into an integrated system. The machine-learning text-to-image model combines query understanding, image generation, and ranking capabilities in a single unified architecture. Additionally, the system integrates supervised fine-tuning and offline caching mechanisms that work together seamlessly, reducing the need for separate processing stages while maintaining comprehensive query processing capability.
Solution Approach 2:
The machine-learning text-to-image model serves multiple functions simultaneously: it generates images from query suggestions, ranks images based on relevance, and adapts to different online platform contexts through supervised fine-tuning. This multi-functional approach eliminates the need for separate specialized components for each task, simplifying the overall system architecture while maintaining versatility.
3Speed
If conventional image selection methods are used, then images can be displayed quickly, but the images are not appealing to users and do not enhance user experience
Solution Approach 1:
The system performs preliminary actions by using offline caching to pre-generate and store images for frequently queried terms. When the same or similar queries are submitted by users, the pre-generated images are retrieved and displayed immediately without requiring real-time generation. This preliminary preparation maintains fast display speeds while ensuring high-quality, appealing images are available when needed.
Solution Approach 2:
The patent implements feedback mechanisms through supervised fine-tuning where the machine-learning model learns from user interactions and engagement data. The system continuously improves image selection and generation based on feedback about which images most effectively represent query suggestions and engage users. This feedback loop ensures that images remain appealing and relevant while maintaining efficient display performance.
Data Source
AI summary
An online system performs an inference task in conjunction with the model serving system and/or interface system to generate relevant product images for query auto-completion and query suggestion to help users better navigate their search experience. The online system generates a collection of query suggestions using search query log mining. For each query suggestion in the collection of query suggestions, the online system retrieves one or more catalog images that depict the query suggestion from a product catalog. The online system constructs a prompt to a text-to-image model including the query suggestion, and a request to generate one or more query images based on the query suggestion. The online system receives the query images from the text-to-image model and ranks the catalog and query images to identify an image to display to the user in association with the query suggestion.