Personalized Image Recognition Using User Profile Data
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Solution Overview
Problem
E-commerce image recognition systems face challenges in accurately differentiating between similar products, such as male and female jeans, and categorizing items that belong to multiple categories, leading to low confidence predictions and errors in user descriptions and search queries.
Innovation Solution
A personalized image recognition scheme that preprocesses input images and user information to generate feature data, determines user groups, and conducts searches in image and user databases to improve prediction accuracy by using both image and user-related data, with confidence value selection for accurate object information generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image recognition techniques are used to identify items from uploaded images, then the system can provide automated item description, but the prediction accuracy is insufficient for difficult-to-differentiate items (e.g., male vs. female jeans) resulting in low confidence predictions
Solution Approach 1:
The patent transitions from traditional single-dimension image recognition to multi-dimensional analysis by incorporating user profile data (gender, age, location, browsing history, purchase history) alongside image features. This dimensional expansion enables the system to disambiguate between similar items by considering the user context, thereby improving both prediction accuracy and confidence levels for difficult-to-differentiate products
Solution Approach 2:
The system dynamically adjusts recognition parameters based on user profiles and behavioral data. By changing the weight and relevance of different image features according to user characteristics (e.g., prioritizing certain features for male users vs. female users), the system adapts to improve prediction accuracy and confidence for items that are difficult to differentiate using standard recognition alone
2Measurement precision
If users manually specify item details for posting or searching, then complete and accurate information can be obtained, but the process is time-consuming and error-prone
Solution Approach 1:
The system enables automated self-service item description by using image recognition to automatically extract and populate item details (category, brand, specifications). This eliminates the need for users to manually type or select each parameter, significantly reducing the time required while maintaining high information accuracy through the system's multi-dimensional recognition approach
Solution Approach 2:
The system uses user feedback from browsing and purchase history to continuously improve and refine item descriptions. By incorporating feedback loops where user interactions inform future recognition accuracy, the system automatically generates increasingly accurate item descriptions without requiring additional user time input
3Measurement precision
If users manually create search queries to find items, then specific search intent can be captured, but the process is tedious and may contain errors
Solution Approach 1:
The system automatically generates search queries based on uploaded images and user profiles, eliminating the need for users to manually construct search strings. The system extracts relevant keywords and parameters from the image and user context, performing the search composition service automatically while maintaining high search accuracy
Solution Approach 2:
The system performs preliminary analysis of user behavior patterns and item characteristics before the actual search is executed. By pre-processing user profiles and image data to identify likely search parameters, the system prepares accurate search queries in advance, improving both search accuracy and user convenience
4Measurement precision
If image recognition is performed without considering user profiles, then the system maintains simplicity and speed, but it cannot accurately differentiate items that belong to multiple categories or are similar across different user preferences
Solution Approach 1:
The system segments the image recognition process into distinct modules: image feature extraction, user profile analysis, and integrated decision-making. By separating these functions and processing them independently before combining results, the system achieves high item differentiation accuracy while managing complexity through modular architecture
Solution Approach 2:
The patent introduces user profile data as an intermediary layer between the image input and the final recognition output. This intermediary contains contextual information about the user that mediates the recognition process, enabling accurate differentiation of similar items without requiring the entire system to become exponentially more complex
Data Source
AI summary
Technologies generally described herein relate to a computing device for personalized image recognition scheme. Example computing devices may include at least one processor; and at least one memory. The at least one memory may store instructions. The at least one processor executes the instructions to perform operations. The operations may comprise obtaining an input image containing an object and a user identifier; preprocessing the input image to produce image feature data; preprocessing user information corresponding to the user identifier to produce user feature data; determining a user group based on the user feature data to obtain group feature data of the user group; conducting a search of an image database based on the image feature data and the group feature data to search for one or more images; and generating object information based on image information of the one or more images.