Card Feature Extraction From Rendered Images for Personalized Ranking
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Solution Overview
Problem
Existing ranking algorithms in search engines and recommendation systems fail to capture user preferences beyond visible layout features, leading to suboptimal presentation of content items, particularly for users with specific preferences or disabilities.
Innovation Solution
Extract image features from digital representations of content items, combining them with user and contextual features to personalize ranking and display algorithms using machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ranking algorithms use only visible layout features and tags, then the system complexity remains low, but the personalization accuracy and user preference capture are insufficient
Solution Approach 1:
The patent creates a visual representation (copy) of the card layout by rendering it as an image. This visual copy captures the actual visible layout features that users perceive, going beyond the abstract tag data. The rendered image serves as a faithful replica of the card's visual structure, enabling image processing techniques to extract meaningful layout features that reflect real user experience.
Solution Approach 2:
The patent replaces traditional mechanical parsing methods (regex-based extraction from HTML/CSS) with image processing techniques. Instead of mechanically scraping layout information from source code, the system renders the card as an image and uses computer vision algorithms to automatically extract layout features. This substitution enables more accurate capture of visual patterns and spatial relationships that directly correspond to user perception.
2Loss of information
If the system extracts comprehensive image features from rendered cards, then the feature extraction capability improves, but the processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential layout features from the rendered card images rather than processing every pixel and detail. By identifying and extracting specific meaningful features such as text regions, image placements, layout patterns, and spatial relationships, the system captures the most important information for personalization while avoiding unnecessary computational overhead from processing redundant visual data.
Solution Approach 2:
The patent segments the card layout into distinct functional regions and feature types (e.g., header sections, content areas, media elements, footer elements). This segmentation allows the image processing system to analyze each region with appropriate algorithms, extracting relevant features from specific areas while skipping unnecessary processing in other areas, thereby reducing overall computational time.
3Adaptability or versatility
If the system incorporates user features and contextual features alongside image features, then the personalization quality improves, but the data processing complexity increases
Solution Approach 1:
The patent merges multiple data sources (image features from rendered cards, user profile data, contextual information) into a unified feature representation. By combining these diverse feature types and feeding them together into machine learning models, the system creates a comprehensive personalization approach that leverages the complementary strengths of each data source while managing complexity through integrated processing.
Solution Approach 2:
The patent creates a universal feature extraction framework that can handle multiple data types (visual, textual, user-profile, contextual) through a common processing architecture. The image processing pipeline serves as a universal approach that can be applied to various card types and content formats, while the same framework accommodates additional user and contextual features, making the system versatile without proportionally increasing complexity.
Data Source
AI summary
Methods and apparatus for using features of images representing content items to improve the presentation of the content items are disclosed. In one embodiment, a plurality of digital images are obtained, where each of the images represents a corresponding one of a plurality of content items Image features of each of the digital images are determined. Additional features including at least one of user features pertaining to a user of a client device or contextual features pertaining to the client device are ascertained. At least a portion of the content items are provided via a network to the client device using features that include or are derived from both the image features of each of the plurality of digital images and the additional features.