Image Ranking System for Online Listings
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Solution Overview
Problem
The process of listing services on online marketplaces is cumbersome, particularly in selecting and displaying images, as users often upload low-quality or irrelevant photos, and there is a need to improve image quality and order to increase booking success, which existing systems fail to address in a scalable and real-time manner.
Innovation Solution
The implementation of a machine learning-based image ranking system that analyzes and ranks images in real-time, determining scene types, visual scores, and diversity to recommend the most attractive image order for listings, leveraging data from millions of listings to train models for scene type classification and visual scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users manually select and upload images for listings, then they have control over image selection, but the image quality and relevance are low and booking success decreases
Solution Approach 1:
The system automatically analyzes, ranks, and selects images without requiring manual user intervention. The image ranking system processes uploaded images, determines their relevance and quality scores, and presents optimized selections to hosts, allowing the system to serve itself in improving image quality while maintaining ease of operation.
Solution Approach 2:
The patent replaces manual image selection (mechanical human operation) with an automated machine learning-based image ranking system. This system uses computational algorithms to evaluate image quality, relevance, and diversity metrics, substituting human judgment with automated technical processes that consistently improve image selection quality.
2Productivity
If users upload multiple images without guidance, then they have flexibility in image selection, but the image order and relevance are not optimized for booking success
Solution Approach 1:
The image ranking system provides feedback to hosts about which images are most relevant and attractive based on analysis of millions of successful listings. The system evaluates images against learned patterns of high-performing listings and communicates recommended selections and rankings back to users, enabling data-driven optimization of image presentation to improve booking success rates.
Solution Approach 2:
The system performs preliminary analysis and ranking of images before they are displayed to potential guests. By pre-processing and optimizing image selection and order based on historical data and machine learning models, the system ensures that the most relevant and attractive images are presented first, maximizing booking potential before user interaction occurs.
3Manufacturing precision
If the system analyzes and ranks images using machine learning, then image quality and booking success improve, but the processing time and computational resources increase
Solution Approach 1:
The machine learning models are trained in advance on millions of existing listings to learn patterns of high-quality, bookable images. This preliminary training phase allows the system to quickly apply learned knowledge to new image uploads without requiring extensive real-time computation, enabling fast image ranking and selection while maintaining high assessment quality.
Solution Approach 2:
The patent replaces complex real-time human image evaluation with pre-trained automated machine learning systems. The computational models, trained offline on large datasets, can rapidly assess image quality and relevance in real-time without requiring extensive processing power during the actual listing creation moment, thus reducing perceived processing time while maintaining assessment precision.
Data Source
AI summary
Systems and methods are provided for generating a base visual score for each candidate image of a plurality of images received by a computing system, based on the scene type of each image. For each candidate image, the computing system multiplies the base visual score by a feature importance weight to generate a first visual score, adds respective scene type bonus points to the first visual score to generate a second visual score, and adds diversity scoring points to the second visual score to generate a final visual score for each candidate image. The computing system ranks the candidate images based on the final visual scores and provides a specified number of the top-ranked candidate images to be displayed on a display of the computing device.