Visual Search Pivot Generation for Intent-Aware Similarity Refinement
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Solution Overview
Problem
Conventional visual search platforms fail to generate pivots that accurately capture a user's search intent, leading to user frustration and increased computational resource consumption due to repeated keyword queries and visual searches.
Innovation Solution
A visual search pivot system employs machine learning models to generate pivots relevant to a seed item and current search context using information such as item attributes, user interactions, and session data, and pre-populates a cache with these pivots for faster retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional visual search platforms use basic keyword matching and simple visual similarity algorithms, then the system complexity remains low, but the search accuracy and ability to capture user intent deteriorates
Solution Approach 1:
The system performs preliminary action by generating and caching pivots before actual search queries are executed. The pivot generation module creates attribute-based pivots from seed items in advance, and these pre-computed pivots are stored in a cache for rapid retrieval during search operations, eliminating the need for complex real-time computation
Solution Approach 2:
The patent introduces pivots as an intermediary element between keyword searches and full visual similarity searches. These pivots serve as intermediate representations that capture essential visual attributes (color, shape, texture, pattern) and enable more accurate search matching without requiring the full complexity of deep visual analysis for every query
2Reliability
If the system generates comprehensive pivots using machine learning models with multiple conditioning signals, then the user intent capture improves, but the computational resource consumption increases
Solution Approach 1:
The system performs pivot generation in advance and caches the results, so that during actual search operations, pre-computed pivots are retrieved rather than regenerated. This shifts computational workload from real-time query processing to offline or batch processing, significantly reducing energy consumption during user interactions
Solution Approach 2:
The machine learning model dynamically adjusts its processing based on the type of seed item received. The system selectively applies different attribute extraction parameters and model complexity levels appropriate to each item category, optimizing computational resource usage by matching processing intensity to actual needs
3Productivity
If the system requires multiple user interactions and repeated queries to refine search results, then the search thoroughness improves, but the user experience and efficiency deteriorates
Solution Approach 1:
By pre-generating pivots and caching them, the system prepares search refinement options in advance, allowing users to immediately access relevant attribute-based filters without waiting for complex computations during their interaction session
Solution Approach 2:
The system implements feedback by analyzing user interactions with search results and using this information to refine future pivot generation. The machine learning model learns from user behavior patterns to improve the relevance and accuracy of generated pivots, creating a continuous improvement loop that enhances search efficiency over time
Data Source
AI summary
In accordance with techniques for visual search pivot generation, a visual search request is received to trigger a visual search for items that are visually similar to a seed item. Using a machine learning model, one or more pivots representing visual attribute values for refining the visual search are generated based on information associated with the seed item. The one or more pivots are communicated for display in a user interface, and a user selection of a pivot is received. In response, at least one item is communicated for display in the user interface that is visually similar to the seed item and has a visual attribute value corresponding to the pivot.