Visual Feedback Query Refinement for Imprecise Item Search
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Solution Overview
Problem
E-retailers face challenges in helping customers select items from large inventories when precise descriptions are hard to articulate, especially for categories like furniture and clothing, as traditional search mechanisms are ineffective due to imprecise item specifications.
Innovation Solution
Implementing a system that uses machine learning models for real-time refinement of query results based on visual feedback, where a deep neural network generates feature sets and a second model re-scores items using feedback signals, allowing for iterative refinement and presentation of better-suited items to the consumer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional search mechanisms are used, then search simplicity is maintained, but search effectiveness deteriorates for categories with imprecise item specifications
Solution Approach 1:
The system implements feedback mechanisms where visual feedback from users (images, videos) is processed by machine learning models to refine search results in real-time. The feedback loop includes: user provides visual feedback -> system processes feedback through ML models -> search results are dynamically refined -> refined results are presented to user. This resolves the contradiction by maintaining simple user interaction while dramatically improving search effectiveness through intelligent feedback processing.
Solution Approach 2:
The patent replaces traditional mechanical search mechanisms (keyword-based text search) with machine learning-based visual search mechanisms. Instead of relying on users to type precise descriptions, the system uses deep neural networks to process visual data and generate search queries automatically, then refines results based on user feedback. This substitution enables effective searching for categories with imprecise specifications without complicating user interaction.
2Productivity
If machine learning models are used for real-time refinement, then search effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments the complex machine learning pipeline into distinct functional modules: visual feedback processing module, feature extraction module, model training module, and result refinement module. Each module handles a specific aspect of the search refinement process independently, making the overall complex system more manageable and maintainable while preserving high search effectiveness.
Solution Approach 2:
The system performs preliminary actions by pre-training machine learning models on large datasets of visual feedback and item characteristics before actual search queries are processed. This pre-training phase prepares the models to efficiently handle real-time search refinement tasks, reducing the computational burden during actual user interactions and making the system more practical despite its inherent complexity.
3Measurement precision
If real-time refinement iterations are performed, then result relevance is improved, but processing time increases
Solution Approach 1:
The system applies partial refinement actions by performing a limited number of refinement iterations rather than exhaustive search. The machine learning models are designed to achieve sufficient result relevance within a constrained number of iterations, balancing processing time and result quality. The system stops refinement when marginal improvements in relevance no longer justify additional processing time, implementing an optimal trade-off between precision and time loss.
Solution Approach 2:
The system dynamically changes processing parameters based on user feedback and query characteristics. The machine learning models adjust the number of refinement iterations, the depth of feature extraction, and the complexity of result scoring based on real-time conditions. This parameter adaptation allows the system to maintain high result relevance while minimizing processing time by allocating computational resources strategically rather than uniformly across all queries.
Data Source
AI summary
In response to a programmatic interaction, respective representations of items of an initial result set are presented to an item consumer. One or more result refinement iterations are then conducted. In a given iteration, one or more feedback indicators with respect to one or more items are identified, a machine learning model is trained using at least the feedback indicators to generate respective result set candidacy metrics for at least some items, and the metrics are then used to transmit additional items for presentation to the item consumer.